122,953 results
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2. Teacher Training on Artificial Intelligence in Education
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Fissore, Cecilia, Floris, Francesco, Conte, Marina Marchisio, Sacchet, Matteo, Ifenthaler, Dirk, Series Editor, Sampson, Demetrios G., Series Editor, Isaías, Pedro, Series Editor, Gibson, David C., Editorial Board Member, Huang, Ronghuai, Editorial Board Member, Kinshuk, Editorial Board Member, and Spector, J. Michael, Editorial Board Member
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- 2024
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3. Proceedings of the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, July 11-14, 2023)
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International Educational Data Mining Society, Feng, Mingyu, Käser, Tanja, and Talukdar, Partha
- Abstract
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for amplifying human potential." Not all students or seekers of knowledge receive the education necessary to help them realize their full potential, be it due to a lack of resources or lack of access to high quality teaching. The dearth in high-quality educational content, teaching aids, and methodologies, and non-availability of objective feedback on how they could become better teachers, deprive our teachers from achieving their full potential. The administrators and policy makers lack tools for making optimal decisions such as optimal class sizes, class composition, and course sequencing. All these handicap the nations, particularly the economically emergent ones, who recognize the centrality of education for their growth. EDM-2023 has striven to focus on concepts, principles, and techniques mined from educational data for amplifying the potential of all the stakeholders in the education system. The spotlights of EDM-2023 include: (1) Five keynote talks by outstanding researchers of eminence; (2) A plenary Test of Time award talk and a Banquet talk; (3) Five tutorials (foundational as well as advanced); (4) Four thought provoking panels on contemporary themes; (5) Peer reviewed technical paper and poster presentations; (6) Doctoral students consortium; and (7) An enchanting cultural programme. [Individual papers are indexed in ERIC.]
- Published
- 2023
4. ChatGPT and Bard in Education: A Comparative Review
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Gustavo Simas da Silva and Vânia Ribas Ulbricht
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ChatGPT and Bard, two chatbots powered by Large Language Models (LLMs), are propelling the educational sector towards a new era of instructional innovation. Within this educational paradigm, the present investigation conducts a comparative analysis of these groundbreaking chatbots, scrutinizing their distinct operational characteristics and applications as depicted in current scholarly discourse. ChatGPT emerges as an exemplary tool in task-oriented textual interactions, while Bard brandishes unique features such as Text-To-Speech (TTS) functionality, which enhances accessibility and inclusive education, as well as integration with Google Workspace applications. This research critically examines their utilization in various spheres such as pedagogy, academic research, Massive Open Online Courses (MOOCs), mathematics, and software programming. Findings accentuate ChatGPT's superior efficacy in content drafting, code generation, language translation, and providing clinically precise responses, notwithstanding Bard's significant potential encapsulated in its exclusive features. Furthermore, the study traverses' crucial ethical aspects, including privacy concerns and inherent bias, underscoring the profound implications of these Artificial Intelligence (AI) technologies on literature and advocating against the indiscriminate reliance on such models. [For the full proceedings, see ED636095.]
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- 2023
5. Yet Another Predictive Model? Fair Predictions of Students' Learning Outcomes in an Online Math Learning Platform
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Li, Chenglu, Xing, Wanli, and Leite, Walter
- Abstract
To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students' artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a generic algorithm that can orchestrate with existing ML algorithms while yielding fairer results. Specifically, we have implemented logistic regression with the Seldonian algorithm and compared the fairness-aware model with fairness-unaware ML models. The results show that the Seldonian algorithm can achieve comparable predictive performance while producing notably higher fairness. [This paper was published in: "LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12-16, 2021, Irvine, CA, USA," ACM, 2021.]
- Published
- 2021
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6. Modeling Consistency Using Engagement Patterns in Online Courses
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Zhou, Jianing and Bhat, Suma
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Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely unexplored. This study focuses on modeling consistency of learners in online courses to address this research gap. Toward this, we propose a novel unsupervised algorithm that combines sequence pattern mining and ideas from information retrieval with a clustering algorithm to first extract engagement patterns of learners, represent learners in a vector space of these patterns and finally group them into groups with similar consistency levels. Using clickstream data recorded in a popular learning management system over two offerings of a STEM course, we validate our proposed approach to detect learners that are inconsistent in their behaviors. We find that our method not only groups learners by consistency levels, but also provides reliable instructor support at an early stage in a course. [This paper was published in: "LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12-16, 2021, Irvine, CA, USA." ACM, 2021, pp. 226-236.]
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- 2021
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7. Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering
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Olney, Andrew M.
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In contrast to simple feedback, which provides students with the correct answer, elaborated feedback provides an explanation of the correct answer with respect to the student's error. Elaborated feedback is thus a challenge for AI in education systems because it requires dynamic explanations, which traditionally require logical reasoning and knowledge engineering to generate. This study presents an alternative approach that formulates elaborated feedback in terms of long-form question answering (LFQA). An off-the-shelf LFQA system was evaluated by human raters in a 2x2x2x2 ablation design that manipulated the context documents given to the LFQA model and the post-processing of model output. Results indicate that context manipulations improve performance but that postprocessing can have detrimental results. [This paper was published in: "Proceedings of the Eighth ACM Conference on Learning @ Scale," 2021, pp. 27-36.]
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- 2021
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8. Recommendation Systems on E-Learning and Social Learning: A Systematic Review
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Souabi, Sonia, Retbi, Asmaâ, Idrissi, Mohammed Khalidi, and Bennani, Samir
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E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems are of widespread concern in online learning, it is still unclear to educators how recommender systems can improve the learning process and have a positive impact on learning. This paper seeks to provide an overview of the recommender systems proposed in e-learning between 2007 and the first part of 2021. Out of 100 initially identified publications for the period between 2007 and the first part of 2021, 51 articles were included for final synthesis, according to specific criteria. The descriptive results show that most of the disciplines involved in educational recommender systems papers have approached e-learning in a general way without putting as much emphasis on social learning, and that recommender systems based on explicit feedbacks and ratings were the most frequently used in empirical studies. The synthesis of results presents several recommender systems types in e-learning: (1) content-based recommender systems; (2) collaborative-filtering recommender systems; (3) hybrid recommender systems; and (4) recommender systems based on supervised and unsupervised algorithms. The conclusions reflect on the almost lack of critical reflection on the importance of addressing recommender systems in social learning and social educational networks in particular, especially as social learning has particular requirements, the weak databases size used in some research work, the importance of acknowledging the strengths and weaknesses of each type of recommender system in an educational context and the need for further exploration of implicit feedbacks more than explicit learners' feedbacks for more accurate recommendations.
- Published
- 2021
9. Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild
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Pugh, Samuel L., Subburaj, Shree Krishna, Rao, Arjun Ramesh, Stewart, Angela E. B., Andrews-Todd, Jessica, and D'Mello, Sidney K.
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We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math problems (35 and 9 dyads in school and lab environments, respectively). Trained coders identified seven cognitive and social CPS skills (e.g., sharing information) in 8,660 utterances. We used a state-of-the-art deep transfer learning approach for NLP, Bidirectional Encoder Representations from Transformers (BERT), with a special input representation enabling the model to analyze adjacent utterances for contextual cues. We achieved a micro-average AUROC score (across seven CPS skills) of 0.80 using ASR transcripts, compared to 0.91 for human transcripts, indicating a decrease in performance attributable to ASR error. We found that the noisy school setting introduced additional ASR error, which reduced model performance (micro-average AUROC of 0.78) compared to the lab (AUROC = 0.83). We discuss implications for real-time CPS assessment and support in schools. [For the full proceedings, see ED615472.]
- Published
- 2021
10. Fair-Capacitated Clustering
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Quy, Tai Le, Roy, Arjun, Friege, Gunnar, and Ntoutsi, Eirini
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Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a "fair-representation" of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make the clusters useful for the end-user, a "balanced cardinality" among the clusters is required. Our motivation comes from the education domain where studies indicate that students might learn better in diverse student groups and of course groups of similar cardinality are more practical e.g., for group assignments. To this end, we introduce the "fair-capacitated clustering problem" that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: (1) we rely on fairlets to generate minimal sets that satisfy the fair constraint; and (2) we propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering. Our experiments on three educational datasets show that our approaches deliver well-balanced clusters in terms of both fairness and cardinality while maintaining a good clustering quality. [For the full proceedings, see ED615472.]
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- 2021
11. MI Theory: Past, Current and Future--A Review of MI Theory in the Past 50 Years
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Zhang, Weiwen
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Recently Prof. Howard Gardner, an outstanding psychologist in the worldwide accepted the interview from Dr. Weiwen Zhang, and talked about a wide range of MI theory and relevant fields, which mainly involved in its core ideas, current situation and future development, and also involved its application in some current hot issues, which gave us important enlightenment in relevant fields.
- Published
- 2020
12. Creating TikToks, Memes, Accessible Content, and Books from Engineering Videos? First Solve the Scene Detection Problem
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Angrave, Lawrence, Li, Jiaxi, and Zhong, Ninghan
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To efficiently create books and other instructional content from videos and further improve accessibility of our course content we needed to solve the scene detection (SD) problem for engineering educational content. We present the pedagogical applications of extracting video images for the purposes of digital book generation and other shareable resources, within the themes of accessibility, inclusive education, universal design for learning and how we solved this problem for engineering education lecture videos. Scene detection refers to the process of merging visually similar frames into a single video segment, and subsequent extraction of semantic features from the video segment (e.g., title, words, transcription segment and representative image). In our approach, local features were extracted from inter-frame similarity comparisons using multiple metrics. These include numerical measures based on optical character recognition (OCR) and pixel similarity with and without face and body position masking. We analyze and discuss the trade-offs in accuracy, performance and computational resources required. By applying these features to a corpus of labeled videos, a support vector machine determined an optimal parametric decision surface to model if adjacent frames were semantically and visually similar or not. The algorithm design, data flow, and system accuracy and performance are presented. We evaluated our system using videos from multiple engineering disciplines where the content was comprised of different presentation styles including traditional paper handouts, Microsoft PowerPoint slides, and digital ink annotations. For each educational video, a comprehensive digital-book composed of lecture clips, slideshow text, and audio transcription content can be generated based on our new scene detection algorithm. Our new scene detection approach was adopted by ClassTranscribe, an inclusive video platform that follows Universal Design for Learning principles. We report on the subsequent experiences and feedback from students who reviewed the generated digital-books as a learning component. We highlight remaining challenges and describe how instructors can use this technology in their own courses. The main contributions of this work are: Identifying why automated scene detection of engineering lecture videos is challenging; Creation of a scene-labeled corpus of videos representative of multiple undergraduate engineering disciplines and lecture styles suitable for training and testing; Description of a set of image metrics and support vector machine-based classification approach; Evaluation of the accuracy, recall and precision of our algorithm; Use of an algorithmic optimization to obviate GPU resources; Student commentary on the digital book interface created from videos using our SD algorithm; Publishing of a labeled corpus of video content to encourage additional research in this area; and an independent open-source scene extraction tool that can be used pedagogically by the ASEE community e.g., to remix and create fun shareable instructional content memes, and to create accessible audio and text descriptions for students who are blind or have low vision. Text extracted from each scene can also used to improve the accuracy of captions and transcripts, improving accessibility for students who are hard of hearing or deaf.
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- 2022
13. Examination of Adaptation Components in Serious Games: A Systematic Review Study
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Aydin, Muharrem, Karal, Hasan, and Nabiyev, Vasif
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This study aims to examine adaptability for educational games in terms of adaptation elements, components used in creating user profiles, and decision algorithms used for adaptation. For this purpose, articles and full-text papers in Web of Science, Google Scholar, and Eric databases between 2000-2021 were searched using the keywords "educational games", "serious games", "game-based learning", "adapt*", "player modeling", "user modeling". After applying the inclusion and exclusion procedures of studies accessed in the search, 26 studies were included in the study. The studies were analyzed in line with the themes determined for the components used in the adaptation of educational games. According to the results, adaptive educational game design was made for a wide variety of fields such as programming teaching, physics, mathematics, computational thinking, and logic. As for adaptive factors; It was determined that adaptations were made for the game, educational content, interface, and non-player character (NPC) behaviors. It is understood that pre-game adaptation and in-game adaptation methods are used as adaptation types. Finally, it is seen that Bayesian networks, artificial neural networks, fuzzy logic, deep learning, item response theory, and decision tree methods are preferred as decision systems in the adaptation process. The findings of this literature review can facilitate the design process by providing a roadmap for researchers interested in adaptive educational game design.
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- 2023
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14. Towards Fair Educational Data Mining: A Case Study on Detecting At-Risk Students
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Hu, Qian and Rangwala, Huzefa
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Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students' performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine learning models, there is increasing evidence and concerns about bias and unfairness of these models. Unfair models can lead to inequitable outcomes for some groups of students and negatively impact their learning. We show by real-world examples that educational data has embedded bias that leads to biased student modeling, which urges the development of fairness formalizations and fair algorithms for educational applications. Several formalizations of fairness have been proposed that can be classified into two types: (i) group fairness and (ii) individual fairness. Group fairness guarantees that groups are treated fairly as a whole, which might not be fair to some individuals. Thus individual fairness has been proposed to make sure fairness is achieved on individual level. In this work, we focus on developing an individually fair model for identifying students at-risk of underperforming. We propose a model which is based on the idea that the prediction for a student (identifying at-risk students) should not be influenced by his/her sensitive attributes. The proposed model is shown to effectively remove bias from these predictions and hence, making them useful in aiding all students. [For the full proceedings, see ED607784.]
- Published
- 2020
15. A Novel Video Recommendation System for Algebra: An Effectiveness Evaluation Study
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Leite, Walter L., Roy, Samrat, Chakraborty, Nilanjana, Michailidis, George, Huggins-Manley, A. Corinne, D'Mello, Sidney K., Faradonbeh, Mohamad Kazem Shirani, Jensen, Emily, Kuang, Huan, and Jing, Zeyuan
- Abstract
This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we developed a system of five categories of video recommendations: (1) Watch new video; (2) Review current topic video with a new tutor; (3) Review segment of current video with current tutor; (4) Review segment of current video with a new tutor; and (5) Watch next video in curriculum sequence. The category of recommendation was determined by student scores on a quiz and a sensor-free engagement detection model. New video recommendations (i.e., category 1) were selected based on a novel reinforcement learning algorithm that takes input from an item response theory model. The recommendation system was evaluated in a large field experiment, both before and after school closures due to the COVID-19 pandemic. The results show evidence of effectiveness of the video recommendation algorithm during the period of normal school operations, but the effect disappears after school closures. Implications for teacher orchestration of technology for normal classroom use and periods of school closure are discussed.
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- 2022
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16. Completeness Based Classification Algorithm: A Novel Approach for Educational Semantic Data Completeness Assessment
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Akhrif, Ouidad, Benfaress, Chaymae, EL Jai, Mostapha, El Bouzekri El Idrissi, Youness, and Hmina, Nabil
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Purpose: The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach: A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings: The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value: The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.
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- 2022
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17. Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension
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Balyan, Renu, McCarthy, Kathryn S., and McNamara, Danielle S.
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This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about literary works. Three types of NLP feature sets: unigrams (single content words), elaborative (new) n-grams, and linguistic features were used to classify idea units (paraphrase, text-based inference, interpretive inference). The most accurate classifications emerged using all three NLP features sets in combination, with accuracy ranging from 0.61 to 0.94 (F = 0.18 to 0.81). Random Forests, which employs multiple decision trees and a bagging approach, was the most accurate classifier for these data. In contrast, the single classifier, Trees, which tends to "overfit" the data during training, was the least accurate. Ensemble classifiers were generally more accurate than single classifiers. However, Support Vector Machines accuracy was comparable to that of the ensemble classifiers. This is likely due to Support Vector Machines' unique ability to support high dimension feature spaces. The findings suggest that combining the power of NLP and machine learning is an effective means of automating literary text comprehension assessment. [This paper was published in: A. Hershkovitz & L. Paquette (Eds.), "Proceedings of the 10th International Conference on Educational Data Mining" (pp. 244-249), Wuhan, China: International Educational Data Mining Society.]
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- 2017
18. Assessing Question Quality Using NLP
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Kopp, Kristopher J., Johnson, Amy M., Crossley, Scott A., and McNamara, Danielle S.
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An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict question quality. NLP indices related to lexical sophistication modestly predicted question type. Accuracies improved when predicting two levels (shallow versus deep). [This paper was published in: E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, & B. du Boulay (Eds.), "Proceedings of the 18th International Conference on Artificial Intelligence in Education" (pp. 523-527). Wuhan, China: Springer.]
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- 2017
19. Algorithmic Bias in Education
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Baker, Ryan S. and Hawn, Aaron
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In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education--which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspectives to the machine learning pipeline, and review metrics for assessing bias. Next, we review the evidence around algorithmic bias in education, beginning with the most heavily-studied categories of race/ethnicity, gender, and nationality, and moving to the available evidence of bias for less-studied categories, such as socioeconomic status, disability, and military-connected status. Acknowledging the gaps in what has been studied, we propose a framework for moving from unknown bias to known bias and from fairness to equity. We discuss obstacles to addressing these challenges and propose four areas of effort for mitigating and resolving the problems of algorithmic bias in AIED systems and other educational technology.
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- 2022
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20. Toward a Taxonomy of Trust for Probabilistic Machine Learning
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Broderick, Tamara, Gelman, Andrew, Meager, Rachael, Smith, Anna L., and Zheng, Tian
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Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in the use of a particular code implementation of the chosen algorithm. We detail how trust can fail at each step and illustrate our taxonomy with two case studies. Finally, we describe a wide variety of methods that can be used to increase trust at each step of our taxonomy. The use of our taxonomy highlights steps where existing research work on trust tends to concentrate and also steps where building trust is particularly challenging. [This paper was published in "Science Advances."]
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- 2022
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21. Educating Software and AI Stakeholders about Algorithmic Fairness, Accountability, Transparency and Ethics
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Bogina, Veronika, Hartman, Alan, Kuflik, Tsvi, and Shulner-Tal, Avital
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This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing the need for such education and identifying the intended consumers of educational materials on the topic. We discuss the topics of greatest concern and in need of educational resources; we also survey the existing materials and past experiences in such education, noting the scarcity of suitable material on aspects of fairness in particular. We use an example of a college admission platform to illustrate our ideas. We conclude with recommendations for further work in the area and report on the first steps taken towards achieving this goal in the framework of an academic graduate seminar course, a graduate summer school, an embedded lecture in a software engineering course, and a workshop for high school teachers.
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- 2022
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22. Teaching Responsible Data Science: Charting New Pedagogical Territory
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Lewis, Armanda and Stoyanovich, Julia
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Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the "object-to-interpret-with," a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of "nutritional labels"--a family of interpretability tools that are gaining popularity in responsible data science research and practice.
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- 2022
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23. Toward the Automatic Labeling of Course Questions for Ensuring Their Alignment with Learning Outcomes
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Supraja, S., Hartman, Kevin, Tatinati, Sivanagaraja, and Khong, Andy W. H.
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Expertise in a domain of knowledge is characterized by a greater fluency for solving problems within that domain and a greater facility for transferring the structure of that knowledge to other domains. Deliberate practice and the feedback that takes place during practice activities serve as gateways for developing domain expertise. However, there is a difficulty in consistently aligning feedback about a learner's practice performance with the intended learning outcomes of those activities -- especially in situations where the person providing feedback is unfamiliar with the intention of those activities. To address this problem, we propose an intelligent model to automatically label opportunities for practice (assessment questions) according to the learning outcomes intended by the course designers. As a proof of concept, we used a reduced version of Bloom's Taxonomy to define the intended learning outcomes. Using a factorial design, we employed term frequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA) to transform questions from text to word weightages with support vector machine (SVM) and extreme learning machine (ELM) to train and automatically label the questions. We trained our models with 120 questions labeled by the subject matter expert of an undergraduate engineering course. Compared to existing works which create models based on a selfgenerated dataset, our proposed approach uses 30 untrained questions from online/textbook sources to validate the performance of our models. Exhaustive comparison analysis of the testing set showed that TF-IDF with ELM outperformed the other combinations by yielding 0.86 reliability (F1 measure) with the subject matter expert. [For the full proceedings, see ED596512.]
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- 2017
24. Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection
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Xue, Linting, Lynch, Collin F., and Chi, Min
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Augmented Graph Grammars are a graph-based rule formalism that supports rich relational structures. They can be used to represent complex social networks, chemical structures, and student-produced argument diagrams for automated analysis or grading. In prior work we have shown that Evolutionary Computation (EC) can be applied to induce empirically-valid grammars for student-produced argument diagrams based upon fitness selection. However this research has shown that while the traditional EC algorithm does converge to an optimal fitness, premature convergence can lead to it getting stuck in local maxima, which may lead to undiscovered rules. In this work, we augmented the standard EC algorithm to induce more heterogeneous Augmented Graph Grammars by replacing the fitness selection with a novelty-based selection mechanism every ten generations. Our results show that this novelty selection increases the diversity of the population and produces better, and more heterogeneous, grammars. [For the full proceedings, see ED596512.]
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- 2017
25. Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension
- Author
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Balyan, Renu, McCarthy, Kathryn S., and McNamara, Danielle S.
- Abstract
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about literary works. Three types of NLP feature sets: unigrams (single content words), elaborative (new) n-grams, and linguistic features were used to classify idea units (paraphrase, text-based inference, interpretive inference). The most accurate classifications emerged using all three NLP features sets in combination, with accuracy ranging from 0.61 to 0.94 (F=0.18 to 0.81). Random Forests, which employs multiple decision trees and a bagging approach, was the most accurate classifier for these data. In contrast, the single classifier, Trees, which tends to "overfit" the data during training, was the least accurate. Ensemble classifiers were generally more accurate than single classifiers. However, Support Vector Machines accuracy was comparable to that of the ensemble classifiers. This is likely due to Support Vector Machines' unique ability to support high dimension feature spaces. The findings suggest that combining the power of NLP and machine learning is an effective means of automating literary text comprehension assessment. [For the full proceedings, see ED596512. For the corresponding grantee submission, see ED577127.]
- Published
- 2017
26. Proceedings of the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, June 25-28, 2017)
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International Educational Data Mining Society, Hu, Xiangen, Barnes, Tiffany, Hershkovitz, Arnon, and Paquette, Luc
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The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer Science and Technology at Tsinghua University; and Dr. Ron Cole, President of Boulder Learning Inc. The main conference invited contributions to the Research Track and Industry Track. 122 submissions were received (71 full, 47 short, 4 industry). 18 full papers papers were accepted (25% acceptance rate) and 32 short papers for oral presentation (42% acceptance rate) and an additional 39 for poster presentations, 3 demonstrations. The industry track includes all 4 submitted industry papers and 1 paper initially submitted as a full paper. The EDM conference provides opportunities for young researchers, and particularly Ph.D. students, to present their research ideas and receive feedback from the peers and more senior researchers. This year, the Doctoral Consortium features 6 such presentations. In addition to the main program, the conference includes 3 workshops: (1) Graph-based Educational Data Mining (G-EDM 2017); (2) Sharing and Reusing Data & Analytics Methods with LearnSphere; and (3) Deep Learning with Educational Data; and 2 tutorials: (1) Why Data Standards are Critical for EDM and AIED; and (2) Principal Stratification for EDM Experiments. [For the 2016 proceedings, see ED592609.]
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- 2017
27. Towards Interpretable Automated Machine Learning for STEM Career Prediction
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Liu, Ruitao and Tan, Aixin
- Abstract
In this paper, we describe our solution to predict student STEM career choices during the 2017 ASSISTments Datamining Competition. We built a machine learning system that automatically reformats the data set, generates new features and prunes redundant ones, and performs model and feature selection. We designed the system to automatically find a model that optimizes prediction performance, yet the final model is a simple logistic regression that allows researchers to discover important features and study their effects on STEM career choices. We also compared our method to other methods, which revealed that the key to good prediction is proper feature enrichment in the beginning stage of the data analysis, while feature selection in a later stage allows a simpler final model.
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- 2020
28. Application of Machine Learning in Higher Education to Assess Student Academic Performance, At-Risk, and Attrition: A Meta-Analysis of Literature
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Fahd, Kiran, Venkatraman, Sitalakshmi, Miah, Shah J., and Ahmed, Khandakar
- Abstract
Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, at-risk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria, including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a small-scale dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics and provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.
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- 2022
- Full Text
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29. Teaching AI Search Algorithms in a Web-Based Educational System
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Grivokostopoulou, Foteini and Hatzilygeroudis, Ioannis
- Abstract
In this paper, we present a way of teaching AI search algorithms in a web-based adaptive educational system. Teaching is based on interactive examples and exercises. Interactive examples, which use visualized animations to present AI search algorithms in a step-by-step way with explanations, are used to make learning more attractive. Practice exercises, which are interactive exercises where immediate feedback is given when a student makes an error, but further help is optional, are also used. So, the student can try by him/herself to correct the error or ask for help from the system. Finally, the student can take a test consisting of assessment exercises, which are interactive, but no help is provided. The result of the test determines the student's knowledge level. Evaluation of the system through a pre-test/post-test and experimental/control group method gave very promising results about learning capabilities of the method. Also, results of a questionnaire show that the majority of the students liked the system very much. [For the full proceedings see ED562127.]
- Published
- 2013
30. E-Learning Software for Improving Student's Music Performance Using Comparisons
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Delgado, M., Fajardo, W., and Molina-Solana, M.
- Abstract
In the last decades there have been several attempts to use computers in Music Education. New pedagogical trends encourage incorporating technology tools in the process of learning music. Between them, those systems based on Artificial Intelligence are the most promising ones, as they can derive new information from the inputs and visualize them in several meaningful ways. This paper presents an application of machine learning to music performance which is able to discover the similarities and differences between a given performance and those from other musicians. Such a system would help students to better learn how to perform a certain piece of music, allowing them to compare with other students or master performers. [For the full proceedings, see ED562127.]
- Published
- 2013
31. Proceedings of the International Conference on Educational Data Mining (EDM) (9th, Raleigh, North Carolina, June 29-July 2, 2016)
- Author
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International Educational Data Mining Society, Barnes, Tiffany, Chi, Min, and Feng, Mingyu
- Abstract
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis 2013, Chania 2012, Eindhoven 2011, Pittsburgh 2010, Cordoba 2009 and Montreal 2008). The EDM conference is the leading international forum for high-quality research that leverages educational data, learning analytics, and machine learning to answer research questions that shed light on the learning processes. This year's conference features three invited talks by: Rakesh Agrawal, President and Founder of Data Insights Laboratories; Marcia C. Linn, Professor of the University of California at Berkeley; and Judy Kay, Professor of the University of Sydney. Judy Kay's invited paper entitled "Enabling people to harness and control EDM for lifelong, life-wide learning" is also presented in the proceedings. Together with the "Journal of Educational Data Mining" ("JEDM"), the EDM 2016 conference supports a "JEDM" Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceedings and to present their work to a live audience. The papers submitted to this track followed the "JEDM" peer review process; three papers have been accepted to the track and were presented at the conference. The abstracts of the invited talks, panels and accepted "JEDM" Track papers can be found in these proceedings. [For the 2015 proceedings, see ED560503.]
- Published
- 2016
32. An Explainable Attention-Based Bidirectional GRU Model for Pedagogical Classification of MOOCs
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Sebbaq, Hanane and El Faddouli, Nour-eddine
- Abstract
Purpose: The purpose of this study is, First, to leverage the limitation of annotated data and to identify the cognitive level of learning objectives efficiently, this study adopts transfer learning by using word2vec and a bidirectional gated recurrent units (GRU) that can fully take into account the context and improves the classification of the model. This study adds a layer based on attention mechanism (AM), which captures the context vector and gives keywords higher weight for text classification. Second, this study explains the authors' model's results with local interpretable model-agnostic explanations (LIME). Design/methodology/approach: Bloom's taxonomy levels of cognition are commonly used as a reference standard for identifying e-learning contents. Many action verbs in Bloom's taxonomy, however, overlap at different levels of the hierarchy, causing uncertainty regarding the cognitive level expected. Some studies have looked into the cognitive classification of e-learning content but none has looked into learning objectives. On the other hand, most of these research papers just adopt classical machine learning algorithms. The main constraint of this study is the availability of annotated learning objectives data sets. This study managed to build a data set of 2,400 learning objectives, but this size remains limited. Findings: This study's experiments show that the proposed model achieves highest scores of accuracy: 90.62%, F1-score and loss. The proposed model succeeds in classifying learning objectives, which contain ambiguous verb from the Bloom's taxonomy action verbs, while the same model without the attention layer fails. This study's LIME explainer aids in visualizing the most essential features of the text, which contributes to justifying the final classification. Originality/value: In this study, the main objective is to propose a model that outperforms the baseline models for learning objectives classification based on the six cognitive levels of Bloom's taxonomy. In this sense, this study builds the bidirectional GRU (BiGRU)-attention model based on the combination of the BiGRU algorithm with the AM. This study feeds the architecture with word2vec embeddings. To prove the effectiveness of the proposed model, this study compares it with four classical machine learning algorithms that are widely used for the cognitive classification of text: Bayes naive, logistic regression, support vector machine and K-nearest neighbors and with GRU. The main constraint related to this study is the absence of annotated data; there is no annotated learning objective data set based on Bloom's taxonomy's cognitive levels. To overcome this problem, this study seemed to have no choice but to build the data set.
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- 2022
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33. Student Profile Modeling Using Boosting Algorithms
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Hamim, Touria, Benabbou, Faouzia, and Sael, Nawal
- Abstract
The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models' performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.
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- 2022
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34. An Innovative Evaluation Method for Undergraduate Education: An Approach Based on 'BP' Neural Network and Stress Testing
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Liu, Chang, Feng, Yongfu, and Wang, Yuling
- Abstract
In this paper, a new evaluation method for under-graduate education quality is proposed based on Artificial Intelligence Neural Network Back-Propagation (BP) algorithm and stress testing. Using this method, a publically available indicator pool is constructed, consisting of 19 variables in 4 dimensions such as Teaching Attitude, Teaching Content, Teaching Approach, and Basic Characteristic of Teachers, which impact under-graduates' mastery of knowledge and capacity building. After the BP neural network algorithm is used to learn the optimum parameters for this evaluation model, sensitivity test is applied to identify the indicators that have significant effects on the quality of education. Furthermore, scenario analysis is utilized to explore the influence of the quality of education under pre-specified situations, which provides theoretical and empirical support for evaluating under-graduate teaching, improving education quality, and enriching teacher resources.
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- 2022
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35. Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor
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International Working Group on Educational Data Mining, Rus, Vasile, Lintean, Mihai, and Azevedo, Roger
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This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge activation, a self-regulatory process. We describe two major categories of methods and combine each method with various machine learning algorithms. A detailed comparison among the methods and across all algorithms is also provided. The evaluation of the proposed methods is performed by comparing the prediction of the methods with human judgments on a set of 309 prior knowledge activation paragraphs collected from previous experiments with MetaTutor on college students. According to our experiments, a content-based method with word-weighting and Bayes Nets algorithm is the most accurate. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
- Published
- 2009
36. EdMedia + Innovate Learning: World Conference on Educational Media and Technology (New York, New York and Online, June 20-23, 2022)
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Association for the Advancement of Computing in Education and Bastiaens, Theo
- Abstract
The Association for the Advancement of Computing in Education (AACE) is an international, non-profit educational organization. The Association's purpose is to advance the knowledge, theory, and quality of teaching and learning at all levels with information technology. The "EdMedia + Innovate Learning" conference took place in New York, New York and online June 20-23, 2022. These proceedings include 180 papers, including 2 award papers. The award papers cover the topics of VALUE (Valid Assessment of Learning in Undergraduate Education) rubrics and teacher candidates' acceptance and intentional use of augmented reality (AR) technology.
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- 2022
37. Do We Betray Errors Beforehand? The Use of Eye Tracking, Automated Face Recognition and Computer Algorithms to Analyse Learning from Errors
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Harteis, Christian, Fischer, Christoph, Töniges, Torben, and Wrede, Britta
- Abstract
Preventing humans from committing errors is a crucial aspect of man-machine interaction and systems of computer assistance. It is a basic implication that those systems need to recognise errors before they occur. This paper reports an exploratory study that utilises eye-tracking technology and automated face recognition in order to analyse test persons' emotional reactions and cognitive load during a computer game and learning through trial and error. Computer algorithms based on machine learning and big data were tested that identify particular patterns of test persons' gaze behaviour and facial expressions that antecede errors in a computer game. The results show that emotions and learning from errors are positively correlated and that gaze behaviour and facial expressions inform about the errors that follow. However, the algorithms still need to be improved through further studies to be suitable for daily use. This research is innovative in its use of mathematical formulae to operationalise learning through errors and the use of computer algorithms to predict errors in human behaviour in trial-and-error situations.
- Published
- 2018
38. A Review Paper on Deep Learning Approach for Crop Yield Prediction Assessment
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Richa Verma Ayushi
- Subjects
business.industry ,Deep learning ,Crop yield ,Agricultural engineering ,Artificial intelligence ,business ,Mathematics - Abstract
Precise assessment of harvest yield is a difficult field of work. The equipment and programming stage to foresee the harvest yield relies on different components like climate, soil fruitfulness, genotype, and different collaborating wards. The assignment is unpredictable inferable from the information that should be gathered in volumes to comprehend crop yield through remote sensor organizations and distant detecting. This paper audits the previous 15 years of exploration work in the improvement of assessing crop yield utilizing profound learning calculations. The meaning of examining progressions utilizing profound learning methods will help in dynamic for foreseeing the harvest yield. The cross breed mix of profound learning with distant detecting and remote sensor organizations can give accuracy agribusiness later on.
- Published
- 2021
39. Deep Learning Forwarding in NDN with a Case Study of Ethernet LAN
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Ayadi, Mohamed Issam, Maizate, Abderrahim, Ouzzif, Mohamm, and Mahmoudi, Charif
- Abstract
In this paper, the authors propose a novel forwarding strategy based on deep learning that can adaptively route interests/data packets through ethernet links without relying on the FIB table. The experiment was conducted as a proof of concept. They developed an approach and an algorithm that leverage existing intelligent forwarding approaches in order to build an NDN forwarder that can reduce forwarding cost in terms of prefix name lookup, and memory requirement in FIB simulation results showed that the approach is promising in terms of cross-validation score and prediction in ethernet LAN scenario.
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- 2021
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40. Introducing Students to Machine Learning with Decision Trees Using CODAP and Jupyter Notebooks
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Biehler, Rolf and Fleischer, Yannik
- Abstract
This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary-school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug-in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu-based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning.
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- 2021
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41. Becoming Information Centric: The Emergence of New Cognitive Infrastructures in Education Policy
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Sellar, Sam and Gulson, Kalervo N.
- Abstract
New cognitive infrastructures are emerging as digital platforms and artificial intelligence enable new forms of automated thinking that shape human decision-making. This paper (a) offers a new theoretical perspective on automated thinking in education policy and (b) illustrates how automated thinking is emerging in one specific policy context. We report on a case study of a policy analysis unit ('The Centre') in an Australian state education department that has been implementing a BI strategy since 2013. The Centre is now focused on using BI to support complex decision making and improve learning outcomes, and their strategy describes this focus as becoming 'information centric'. The theoretical framework for our analysis draws on infrastructure studies and philosophy of technology, particularly Luciana Parisi's recent work on automated thinking. We analyse technical documentation and semi-structured interview data to describe the enactment of a BI strategy in The Centre, with a focus on how new approaches to data analytics are shaping decision-making. Our analysis shows that The Centre is developing a cognitive infrastructure that is already creating new conditions for education policy making, and we conclude with a call for research designs that enable pragmatic exploration of what these infrastructures can do.
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- 2021
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42. Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (Madrid, Spain, October 19-21, 2012)
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International Association for Development of the Information Society (IADIS)
- Abstract
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a fast pace and affecting academia and professional practice in many ways. Paradigms such as just-in-time learning, constructivism, student-centered learning and collaborative approaches have emerged and are being supported by technological advancements such as simulations, virtual reality and multi-agents systems. These developments have created both opportunities and areas of serious concerns. This conference aimed to cover both technological as well as pedagogical issues related to these developments. The IADIS CELDA 2012 Conference received 98 submissions from more than 24 countries. Out of the papers submitted, 29 were accepted as full papers. In addition to the presentation of full papers, short papers and reflection papers, the conference also includes a keynote presentation from internationally distinguished researchers. Individual papers contain figures, tables, and references.
- Published
- 2012
43. Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods
- Author
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Seon Hwa Jeong, Kyung Ju Jang, and Tae Young Heo
- Subjects
Environmental Engineering ,business.industry ,Near-infrared spectroscopy ,Bioengineering ,Pattern recognition ,Raw material ,Linear discriminant analysis ,Random forest ,Support vector machine ,Statistical classification ,Partial least squares regression ,Artificial intelligence ,Multivariate statistical ,business ,Waste Management and Disposal ,Mathematics - Abstract
Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.
- Published
- 2020
44. Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)
- Author
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International Working Group on Educational Data Mining, Barnes, Tiffany, Desmarais, Michel, Romero, Cristobal, and Ventura, Sebastian
- Abstract
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) A Comparison of Student Skill Knowledge Estimates (Elizabeth Ayers, Rebecca Nugent, Nema Dean); (2) Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task (Ryan S.J.d. Baker); (3) A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials (Dror Ben-Naim, Michael Bain, and Nadine Marcus); (4) Detecting Symptoms of Low Performance Using Production Rules (Javier Bravo and Alvaro Ortigosa); (5) Predicting Students Drop Out: A Case Study (Gerben W. Dekker, Mykola Pechenizkiy and Jan M. Vleeshouwers); (6) Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning (Mingyu Feng, Joseph E. Beck and Neil T. Heffernan); (7) Does Self-Discipline impact students' knowledge and learning? (Yue Gong, Dovan Rai, Joseph E. Beck, and Neil T. Heffernan); (8) Consistency of Students' Pace in Online Learning (Arnon Hershkovitz and Rafi Nachmias); (9) Student Consistency and Implications for Feedback in Online Assessment Systems (Tara M. Madhyastha and Steven Tanimoto); (10) Edu-mining for Book Recommendation for Pupils (Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa, and Koichiro Morihiro); (11) Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students (Rebecca Nugent, Elizabeth Ayers, and Nema Dean); (12) Determining the Significance of Item Order In Randomized Problem Sets (Zachary A. Pardos and Neil T. Heffernan); (13) Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models (Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinger); (14) Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments (David Nadler Prata, Ryan S.J.d. Baker, Evandro d.B. Costa, Carolyn P. Rose, Yue Cui, Adriana M.J.B. de Carvalho); (15) Using Dirichlet priors to improve model parameter plausibility (Dovan Rai, Yue Gong, Joseph E. Beck); (16) Reducing the Knowledge Tracing Space (Steven Ritter, Thomas K. Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray, and Brendon Towle); (17) Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor (Vasile Rus, Mihai Lintean, and Roger Azevedo); (18) Automatic Concept Relationships Discovery for an Adaptive E-course (Marian Simko, Maria Bielikova); (19) Unsupervised MDP Value Selection for Automating ITS Capabilities (John Stamper and Tiffany Barnes); (20) Recommendation in Higher Education Using Data Mining Techniques (Cesar Vialardi, Javier Bravo Agapito, Leila Shafti, Alvaro and Ortigosa); (21) Developing an Argument Learning Environment Using Agent-Based ITS (ALES) (Safia Abbas and Hajime Sawamura); (22) A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (Antonio R. Anaya and Jesus G. Boticario); (23) Dimensions of Difficulty in Translating Natural Language into First-Order Logic (Dave Barker-Plummer, Richard Cox, and Robert Dale); (24) Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems (Suleyman Cetintas, Luo Si, Yan Ping Xin, and Casey Hord); (25) Back to the future: a non-automated method of constructing transfer models (Ming Feng and Joseph Beck); (26) How do Students Organize Personal Information Spaces? (Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman, and Rafi Nachmias); (27) Improving Student Question Classification (Cecily Heiner and Joseph L. Zachary); (28) Why, What, and How to Log? Lessons from LISTEN (Jack Mostow and Joseph E. Beck); (29) Process Mining Online Assessment Data (Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst, and Paul De Bra); (30) Obtaining Rubric Weights For Assessments By More Than One Lecturer Using A Pairwise Learning Model (J. R. Quevedo and E. Montanes); (31) Collaborative Data Mining Tool for Education (Enrique Garcia, Cristobal Romero, Sebastian Ventura, Miguel Gea, and Carlos de Castro); (32) Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming (Amelia Zafra and Sebastian Ventura); and (33) Visualization of Differences in Data Measuring Mathematical Skills (Lukas Zoubek and Michal Burda). Individual papers contain tables, figures, footnotes, references and appendices.
- Published
- 2009
45. Adaptive Recommendation System Using Machine Learning Algorithms for Predicting Student's Best Academic Program
- Author
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Ezz, Moham and Elshenawy, Ayman
- Abstract
Some of the educational organizations have multi-education paths such as engineering and medicine collages. In such colleges, the behavior of the student in the preparatory year determines which education path the student will join in the future. In this paper, an adaptive recommendation system is proposed for predicting a suitable education path(s) for a student in college preparatory year. The adaptability is achieved by automatically applying different data mining techniques for extracting relevant features and building a tailor-made model for each education path. The problem formulated as a multi-label multi-class binary classification problem and the dataset automatically translated into one-versus-all (for binary classification). As a case study, the proposed model is applied to predict student's academic performance in the faculty of engineering at AL-Azhar University. It recommends a suitable engineering department among seven engineering departments for each student based on his academic performance. The data of each department (i.e. educational program) is fed to the recommendation system. Then, the relevant set of features for each department is selected and a machine learning algorithm with the best performance is selected for the recommendation process of each department. The obtained results showed that the proposed model recommends the best machine learning algorithm (i.e. model) for each faculty department, find the relevant data that are important in the recommendation process and recommend the student with the suitable engineering department(s) with high accuracy.
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- 2020
- Full Text
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46. Proceeding of the International Scientific Colloquium: MATHEMATICS AND CHILDREN (How to Teach and Learn Mathematics) (Osijek, Croatia, April 13, 2007)
- Author
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Pavlekovic, Margita
- Abstract
The main aim of the Organisational Committee of the international scientific colloquium Mathematics and Children is to encourage additional scientific research in the field of mathematics teaching in Croatia. The development of science and education is a part of a long-term Education Sector Development Plan 2005-2010. Following the example of Europe and the rest of the world, special attention in the field of education is given to mathematical literacy of children (PISA programme) as well as to mathematics teacher training (quality insurance in higher education). Mathematics teaching in Croatia faces modified strategic, organizational, social and technical conditions. Introducing one-shift classes in primary schools, including children with special needs (talented ones and those with difficulties) in regular classes, extended day program for all students, two teachers per class, greater mobility of children and teachers in schools and new teaching technologies demand changes in the methodology of mathematical education of both children and future teachers of mathematics. It is important to develop a life-long learning programme for teachers of mathematics that includes doctoral studies. Research in the field of mathematics teaching implies multi- and interdisciplinarity. Therefore a cooperation with scientists outside the field of mathematics (psychologists, special-ed teachers, educators) is an imperative, although we strongly believe that improvements in mathematics teaching should be encouraged within the field of mathematics. A precondition for developing new approaches and methodologies in mathematics teaching in Croatia is a first-hand experience with the results of international research and standards in mathematics teaching and defining doctoral studies within the same field. We believe that the lectures, discussions and experience exchange between Croatian and international participants of the Mathematics and Children meeting will initiate and intensify scientific cooperation in the field of mathematics teaching on the international level. We would also like for this event to initiate the start of doctoral studies in the field of mathematics teaching in Croatia following the examples from Europe and worldwide. We are very grateful to numerous Croatian and international scientists who have recognized the importance of this event and managed to find the time to attend this gathering. We would also like to thank the heads and entrepreneurs of the local community who financed this event for the most part. Papers include: (1) An Overview of the Authorised Curriculum in Teaching Mathematics Harmonised with the Bologna Declaration at the Department of Mathematics, University of Sarajevo (Sefket Arslanagic); (2) Role of Different Representations of Mathematical Concepts for Learning with Understanding (Tatjana Hodnik-Cadez); (3) The Scientific Frameworks of Teaching Mathematics (Zdravko Kurnik); (4) An Evergreen Problem (Emil Molnar); (5) Mathematically Gifted Children: What Can We Teach Them and What Can We Learn? (Vesna Vlahovic-Stetic); (6) Difficulties in Teaching Mathematics in the Second Grade of Primary School (Josip Cindric and Maja Cindric); (7) Children and Simple Combinatorial Situations (Maja Cotic and Darjo Felda); (8) National Curriculum Framework for Primary Mathematics Education--European Experiences and Trends (Aleksandra Cizmesija); (9) Dynamic Mathematics Class and the Smart Board (Sasa Duka and Damir Tomic); (10) The Dyscalculic Child, Mathematics and Teacher Study Students (Lidija Goljevacki and Aleksandra Krampac-Grljusic); (11) Is the Language of Mathematics Difficult? (The level of technical language use among teacher training college students) (Eva Kopasz); (12) Assessment and Evaluation in Mathematics Education (Zeljka Milin-Sipus); (13) Origami and Mathematics (Franka Miriam-Bruckler); (14) Attitudes of the Students of Teaching Studies towards Mathematics (Irena Misurac-Zorica); (15) Partnership among Faculties, Schools and Families for the Improvement of Mathematics Education of the Gifted Children (Ksenija Mogus and Silvija Mihaljevic); (16) Expert System for Detecting a Child's Gift in Mathematics (Margita Pavlekovic, Marijana Zekic-Susac, and Ivana Durdevic); (17) Boris Pavkovic (portrait of a distinguished methodologist and popularizer of mathematics) (Mirko Polonijo); (18) Mathematics in Play and Leisure Activities--LEGO Building Bricks (Tomislav Rudec); (19) Basic Knowledge of Mathematics and Teacher Training (Sanja Rukavina); (20) Solving Linear Equations Using Computer's Drawing Tools (Miljenko Stanic); (21) Developing the Problem-Solving Skills of Children Suffering from Dyscalculia through Mathematical Tasks with a Text (Aniko Straubingerne Kemler); (22) The Concept of the Square and the Rectangle at the Age 10-11 (Ibolya Szilagyne Szinger); (23) The Use of Computers in Teaching Mathematics (Sanja Varosanec); and (24) From Active Experimenting to Abstract Notion Concept (Amalija Zakelj and Aco Cankar). (Individual papers contain tables, graphs, and references.) [Papers are presented in both English and Croatian. These proceedings were published by the University Josip Juraj Strossmayer in Osijek, Faculty of Philosophy in Osijek. Abstract was modified to meet ERIC guidelines
- Published
- 2007
47. New criteria for the characterization of traditional East Asian papers
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Michel Sablier, Chiara Avataneo, Centre de Recherche pour la Conservation des Collections (CRCC), Centre de Recherche sur la Conservation (CRC ), and Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Ministère de la Culture et de la Communication (MCC)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Ministère de la Culture et de la Communication (MCC)
- Subjects
Paper ,Health, Toxicology and Mutagenesis ,Broussonetia kazinoki ,Edgeworthia ,01 natural sciences ,Asian paper ,Gas Chromatography-Mass Spectrometry ,[CHIM.ANAL]Chemical Sciences/Analytical chemistry ,triterpenes ,Botany ,Environmental Chemistry ,East Asia ,Mathematics ,biology ,Terpenes ,010405 organic chemistry ,business.industry ,010401 analytical chemistry ,Pattern recognition ,General Medicine ,[SHS.ART]Humanities and Social Sciences/Art and art history ,Broussonetia ,pyrolysis ,biology.organism_classification ,Wikstroemia sikokiania ,Pollution ,0104 chemical sciences ,Archaeology ,Gampi ,Cultural heritage ,Plant species ,Edgeworthia chrysantha ,Artificial intelligence ,Wikstroemia ,business - Abstract
International audience; We report a pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) method capable of analyzing traditional East Asian papers. The method proposed is based on rapid and easy single step Py-GC/MS analysis that can be carried out with a minimum amount of matter, in the few g range. Three reference papers manufactured from kozo (Broussonetia kazinoki Siebold & Zucc.), mitsumata (Edgeworthia chrysantha Lindl.) and gampi (Wikstroemia sikokiana Franch. & Sav.) with the traditional hand paper making processes were examined. The method allows discrimination between terpenic and steroid compounds, which were revealed as chemical markers of origin of the plant fibers. Each paper investigated was found to have characteristic pyrolysis fingerprints that were unique to the traditional handmade paper, demonstrating the potential for differentiation of these biochemical components of fiber plants on East Asian papers towards identification and conservation of cultural heritage. The investigation on Py-GC/MS was extended to liquid extraction followed by GC/MS analysis to characterize the biochemical components of fiber plants. The main contribution of this study is to provide molecular criteria for discriminating plant species used for traditional East Asian hand papermaking. Py-GC/MS complements efficiently microscope identification especially for adverse cases. A case study of archaeological Chinese paper painting artefacts was thereafter successfully investigated to address informative potential and efficiency of the criteria of identification on ancient and degraded East Asian paperworks.
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- 2016
48. Designing Educational Technologies in the Age of AI: A Learning Sciences-Driven Approach
- Author
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Luckin, Rosemary and Cukurova, Mutlu
- Abstract
Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter-stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme.
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- 2019
- Full Text
- View/download PDF
49. Discrimination of Various Paper Types Using Diffuse Reflectance Ultraviolet–Visible Near-Infrared (UV-Vis-NIR) Spectroscopy: Forensic Application to Questioned Documents
- Author
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Vinay Kumar, Raj Kumar, and Vishal Sharma
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Paper ,Questioned document examination ,Principal Component Analysis ,Spectroscopy, Near-Infrared ,business.industry ,Forensic Sciences ,Pattern recognition ,medicine.disease_cause ,Plot (graphics) ,Spectral line ,Optics ,Principal component analysis ,medicine ,Diffuse reflection ,Artificial intelligence ,Spectroscopy ,business ,Instrumentation ,Ultraviolet ,Arithmetic mean ,Mathematics - Abstract
Diffuse reflectance ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy is applied as a means of differentiating various types of writing, office, and photocopy papers (collected from stationery shops in India) on the basis of reflectance and absorbance spectra that otherwise seem to be almost alike in different illumination conditions. In order to minimize bias, spectra from both sides of paper were obtained. In addition, three spectra from three different locations (from one side) were recorded covering the upper, middle, and bottom portions of the paper sample, and the mean average reflectivity of both the sides was calculated. A significant difference was observed in mean average reflectivity of Side A and Side B of the paper using Student's pair t-test. Three different approaches were used for discrimination: (1) qualitative features of the whole set of samples, (2) principal component analysis, and (3) a combination of both approaches. On the basis of the first approach, i.e., qualitative features, 96.49% discriminating power (DP) was observed, which shows highly significant results with the UV-Vis-NIR technique. In the second approach the discriminating power is further enhanced by incorporating the principal component analysis (PCA) statistical method, where this method describes each UV-Vis spectrum in a group through numerical loading values connected to the first few principal components. All components described 100% variance of the samples, but only the first three PCs are good enough to explain the variance (PC1 = 51.64%, PC2 = 47.52%, and PC3 = 0.54%) of the samples; i.e., the first three PCs described 99.70% of the data, whereas in the third approach, the four samples, C, G, K, and N, out of a total 19 samples, which were not differentiated using qualitative features (approach no. 1), were therefore subjected to PCA. The first two PCs described 99.37% of the spectral features. The discrimination was achieved by using a loading plot between PC1 and PC2. It is therefore concluded that maximum discrimination of writing, office, and photocopy paper could be achieved on the basis of the second approach. Hence, the present inexpensive analytical method can be appropriate for application to routine questioned document examination work in forensic laboratories because it provides nondestructive, quantitative, reliable, and repeatable results.
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- 2015
50. Physics driven behavioural clustering of free-falling paper shapes
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Fumiya Iida, Toby Howison, Josie Hughes, Fabio Giardina, Howison, Toby [0000-0001-8548-5550], Iida, Fumiya [0000-0001-9246-7190], and Apollo - University of Cambridge Repository
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Inertia ,Physiology ,Physical system ,Social Sciences ,computer.software_genre ,Systems Science ,01 natural sciences ,010305 fluids & plasmas ,Physical Phenomena ,Physical phenomena ,Medicine and Health Sciences ,Psychology ,Cluster Analysis ,Moment of Inertia ,Multidisciplinary ,Applied Mathematics ,Simulation and Modeling ,theoretical model ,article ,Classical Mechanics ,Dynamical Systems ,Variety (cybernetics) ,Free falling ,machine learning ,Physical Sciences ,Medicine ,physics ,Algorithms ,Research Article ,Paper ,Computer and Information Sciences ,Reynolds Number ,Science ,Fluid Mechanics ,Research and Analysis Methods ,Machine learning ,Continuum Mechanics ,Motion ,Machine Learning Algorithms ,Artificial Intelligence ,0103 physical sciences ,010306 general physics ,Set (psychology) ,Cluster analysis ,Behavior ,Biological Locomotion ,business.industry ,Biology and Life Sciences ,Fluid Dynamics ,Models, Theoretical ,Nonlinear Dynamics ,Artificial intelligence ,business ,computer ,Mathematics - Abstract
Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Published
- 2019
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