124,450 results
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2. Paper Perfect: Robert Lang and the Science of Origami
- Author
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Foer, Joshua
- Published
- 2014
3. Automatic Test Paper Generation Technology for Mandarin Based on Hilbert Huang Algorithm.
- Author
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Wang, Lei
- Subjects
ARTIFICIAL neural networks ,ALGORITHMS ,COMPUTER engineering ,EMPLOYEE rights ,HUMAN resources departments - Abstract
With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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4. GPTZero vs. Text Tampering: The Battle That GPTZero Wins
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David W. Brown and Dean Jensen
- Abstract
The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may be at risk. The bot's ability to quickly create high quality papers, sometimes complete with reference material, has led to concern that these programs will make students too reliant on their ability and not develop the critical thinking skills necessary to succeed. The rise in these applications has led to the need for the development of detection programs that are able to read the students submitted work and return an accurate estimation of if the paper is human or computer created. These detection programs use natural language processing's (NLP) ideas of perplexity, or randomness of the text, and burstiness, or the tendency for certain words and phrases to appear together, plus sophisticated algorithms to compare the essays to preexisting literature to generate an accurate estimation on the likely author of the paper. The use of these systems has been found to be highly effective in reducing plagiarism among students, however concerns have been raised about the limitations of these systems. False positives, false negatives, and cross language identification are three areas of concern amongst faculty and have led to reduced usage of the detection engines. Despite the limitations however, these systems are a valuable tool for educational institutions to maintain academic integrity and ensure that students are submitting original work. [For the full proceedings, see ED656038.]
- Published
- 2023
5. Cooperative Multiobjective Decision Support for the Paper Industry
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Murthy, Sesh, Akkiraju, Rama, Goodwin, Richard, Keskinocak, Pinar, Rachlin, John, Wu, Frederick, Yeh, James, Fuhrer, Robert, Kumaran, Santhosh, Aggarwal, Alok, Sturzenbecker, Martin, Jayaraman, Ranga, and Daigle, Robert
- Published
- 1999
6. Special Issue Paper: Robust Solutions and Risk Measures for a Supply Chain Planning Problem under Uncertainty
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Poojari, C. A., Lucas, C., and Mitra, G.
- Published
- 2008
7. Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
- Author
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Vassoyan, Jean and Vie, Jill-Jênn
- Abstract
Adaptive learning is an area of educational technology that consists in delivering personalized learning experiences to address the unique needs of each learner. An important subfield of adaptive learning is learning path personalization: it aims at designing systems that recommend sequences of educational activities to maximize students' learning outcomes. Many machine learning approaches have already demonstrated significant results in a variety of contexts related to learning path personalization. However, most of them were designed for very specific settings and are not very reusable. This is accentuated by the fact that they often rely on non-scalable models, which are unable to integrate new elements after being trained on a specific set of educational resources. In this paper, we introduce a flexible and scalable approach towards the problem of learning path personalization, which we formalize as a reinforcement learning problem. Our model is a sequential recommender system based on a graph neural network, which we evaluate on a population of simulated learners. Our results demonstrate that it can learn to make good recommendations in the small-data regime. [For the complete proceedings, see ED630829.]
- Published
- 2023
8. To Speak or Not to Speak, and What to Speak, When Doing Task Actions Collaboratively
- Author
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Nasir, Jauwairia, Kothiyal, Aditi, Sheng, Haoyu, and Dillenbourg, Pierre
- Abstract
Transactive discussion during collaborative learning is crucial for building on each other's reasoning and developing problem solving strategies. In a tabletop collaborative learning activity, student actions on the interface can drive their thinking and be used to ground discussions, thus affecting their problem-solving performance and learning. However, it is not clear how the interplay of actions and discussions, for instance, how students performing actions or pausing actions while discussing, is related to their learning. In this paper, we seek to understand how the transactivity of actions and discussions is associated with learning. Specifically, we ask what is the relationship between discussion and actions, and how it is different between those who learn (gainers) and those who do not (non-gainers). We present a combined differential sequence mining and content analysis approach to examine this relationship, which we applied on the data from 32 teams collaborating on a problem designed to help them learn concepts of minimum spanning trees. We found that discussion and action occur concurrently more frequently among gainers than non-gainers. Further we find that gainers tend to do more reflective actions along with discussion, such as looking at their previous solutions, than non-gainers. Finally, gainers discussion consists more of goal clarification, reflection on past solutions and agreement on future actions than non-gainers, who do not share their ideas and cannot agree on next steps. Thus this approach helps us identify how the interplay of actions and discussion could lead to learning, and the findings offer guidelines to teachers and instructional designers regarding indicators of productive collaborative learning, and when and how, they should intervene to improve learning. Concretely, the results suggest that teachers should support elaborative, reflective and planning discussions along with reflective actions. [For the complete proceedings, see ED630829.]
- Published
- 2023
9. Maching Learning Based Financial Management Mobile Application to Enhance College Students' Financial Literacy
- Author
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Mohsina Kamarudeen and K. Vijayalakshmi
- Abstract
This paper presents a mobile application aimed at enhancing the financial literacy of college students by monitoring their spending patterns and promoting better decision-making. The application is developed using the agile methodology with Android Studio and Flutter as development tools and Firebase as a database. The app is divided into sub-applications, with the home page serving as the program's integration point, displaying a summary of the user's financial progress. The app generates valuable insights into the user's current and future financial success, utilizing data analytics and machine learning to provide detailed and summary insights into the user's financial progress. The machine-learning algorithm used in this app is linear regression, which predicts the user's income and expenses for the upcoming month based on their historical spending data. In addition, the app highlights deals and student discounts in the user's vicinity and links to financial articles that promote better financial planning and decision-making. By promoting responsible spending habits and providing valuable financial insights, this mobile application aims to help students become financially literate and make informed financial decisions for future. [For the full proceedings, see ED654100.]
- Published
- 2023
10. A fully-automated paper ECG digitisation algorithm using deep learning.
- Author
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Wu, Huiyi, Patel, Kiran Haresh Kumar, Li, Xinyang, Zhang, Bowen, Galazis, Christoforos, Bajaj, Nikesh, Sau, Arunashis, Shi, Xili, Sun, Lin, Tao, Yanda, Al-Qaysi, Harith, Tarusan, Lawrence, Yasmin, Najira, Grewal, Natasha, Kapoor, Gaurika, Waks, Jonathan W., Kramer, Daniel B., Peters, Nicholas S., and Ng, Fu Siong
- Subjects
- *
DEEP learning , *ELECTROCARDIOGRAPHY , *ELECTRONIC paper , *ATRIAL fibrillation , *ALGORITHMS , *HEART failure , *HEART rate monitors - Abstract
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Proceedings of Selected Research Paper Presentations at the 1980 Convention of the Association for Educational Communications and Technology and Sponsored by the Research and Theory Division (Denver, CO, April 21-24, 1980).
- Author
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Simonson, Michael R. and Rohner, Daniel
- Abstract
The 31 papers in this collection represent approximately 35 percent of the manuscripts which were submitted for consideration to the Research and Theory Division of the Association for Educational Communications and Technology (AECT) for presentation at the 1980 AECT convention. All papers were subjected to a blind reviewing process and the ones finally selected represent some of the most current thinking in educational communications and technology. A listing of selected titles indicates the scope of the research paper presentations: "The Cognitive Effect in Bilingual Learners Given Different Pictorial Elaboration and Memory Tasks,""The Relationship of Communication Apprehension Level and Media Competency,""Implications of a Gestalt Approach to Research in Visual Communications,""Research on Pictures and Instructional Texts: Difficulties and Directions,""Imagery--A Return to Empirical Investigation,""A Meta-Analytic Study of Pictorial Stimulus Complexity,""Learner Interest and Instructional Design: A Conceptual Model,""The Organizing Function of Behavioral Objectives," and "Algorithmic Training for a Complex Perceptual-Motor Task." (LLS)
- Published
- 1980
12. Analysis of the Correlation between the Use of Written Algorithms and Success in Mental Calculation
- Author
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Juric, Josipa
- Abstract
This paper explores the correlation between mental calculation performance and the frequency of using written algorithms in mental calculation tasks. Mental calculation is a mathematical tool used in everyday life situations during and after our formal education. After presenting an overview of the professional literature on this topic, the paper will present calculation methods and show how represented they are in the Curriculum. For the empirical part of the research, a total of 233 Croatian students aged 10 to 22 years were tested and interviewed. The previously mentioned correlation was then analyzed. An overview of the interview results will be presented as well. It was found that school mathematics does not always contribute to the development and flexibility in using mental calculation strategies because of the student preference for acquired written algorithms. Definitely, recommendation is shifting the focus from written calculation and procedures to the mental, discussing the associated strategies and different concepts of number. In this way, formal education could contribute to what students really need later on, in both private and professional situations in which they may find themselves on a daily basis. [For the full proceedings, see ED630948.]
- Published
- 2022
13. Dialogism Meets Language Models for Evaluating Involvement in CSCL Conversations
- Author
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Maria-Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu, Danielle S. McNamara, and Stefan Trausan-Matu
- Abstract
The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and stimulating collaboration between participants has become more stringent, as human monitoring of the increasing volume of conversations becomes overwhelming. This paper introduces a method grounded in dialogism for evaluating students' involvement in chat conversations based on semantic chains computed using language models. These semantic chains reflect emergent voices from dialogism that span and interact throughout the conversation. Our integrated method uses contextual information captured by BERT transformer models to identify links in a chain that connects semantically related concepts from a voice uttered by one or more participants. Two types of visualizations were generated to depict the longitudinal propagation and the transversal inter-animation of voices within the conversation. In addition, a list of handcrafted features derived from the constructed chains and computed for each participant is introduced. Several machine learning algorithms were tested using these features to evaluate the extent to which semantic chains are predictive of student involvement in chat conversations. [This paper was published in: "Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education, Proceedings of the 6th International Conference on Smart Learning Ecosystems and Regional Development," edited by Ó. Mealha et al., Springer Nature Singapore Pte Ltd., 2022, pp. 67-78.]
- Published
- 2022
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14. Extractive Summarization Using Cohesion Network Analysis and Submodular Set Functions
- Author
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Cioaca, Valentin Sergiu, Dascalu, Mihai, and McNamara, Danielle S.
- Abstract
Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source "ReaderBench" framework, which can be retrofit to cover more than 50 languages. While considering the extensibility of the approach and the problem of missing labeled data for training in various languages besides English, an unsupervised algorithm was preferred to perform extractive summarization (i.e., select the most representative sentences from the original document). Specifically, two different approaches relying on text cohesion were implemented:(1) a graph-based text representation derived from Cohesion Network Analysis that extends TextRank; and (2) a class of submodular set functions. Evaluations were performed on the DUC dataset and use as baseline the implementation of TextRank from Gensim. Our results using the submodular set functions outperform the baseline. In addition, two use cases on English and Romanian languages are presented, with corresponding graphical representations for the two methods. [This paper was published in: 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Proceedings, 2020, pp. 161-168 (ISBN 978-1-7281-7628-4).]
- Published
- 2021
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15. A Comparison of Machine Learning Algorithms for Predicting Student Performance in an Online Mathematics Game
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Lee, Ji-Eun, Jindal, Amisha, Patki, Sanika Nitin, Gurung, Ashish, Norum, Reilly, and Ottmar, Erin
- Abstract
This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students' first mathematical transformation was the most predictive of their math knowledge scores. Implications for game learning analytics and supporting students' algebraic learning are discussed based on the findings.
- Published
- 2022
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16. Servis povezan s prijelomom Hrvatskog društva za fizikalnu i rehabilitacijsku medicinu Hrvatskoga liječničkog zbora – dokument o stajalištu.
- Author
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Grazio, Simeon, Nikolić, Tatjana, Luke Vrbanić, Tea Schnurrer, Poljičanin, Ana, and Grubišić, Frane
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Copyright of Lijecnicki Vjesnik is the property of Croatian Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
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17. Computing Science: Pencil, Paper, and Pi
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Hayes, Brian
- Published
- 2014
18. Exploring Semi-Supervised Learning for Audio-Based Automated Classroom Observations
- Author
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International Association for Development of the Information Society (IADIS), Chanchal, Akchunya, and Zualkernan, Imran
- Abstract
Systematic classroom observation is often used in evaluating and enhancing the quality of classroom instruction. However, classroom observation can potentially suffer from human bias. In addition, the traditional classroom observation is too expensive for resource-constrained environments (e.g., Sub-Saharan Africa, South and Central Asia). A cost-effective automation of classroom observation could potentially enhance both quality and resolution of feedback to the teacher, and hence potentially result in enhancing quality of instruction. Audio-based automatic classroom observation using supervised deep learning techniques has yielded good results in limited contexts. However, one challenge when using supervised techniques is the high cost of collecting and labelling the classroom audio data. One solution for such data-starved scenarios is to use semi-supervised learning (SSL) which requires significantly lesser data and labels. This paper explores an audio-adaptation of the state-of-the-art SSL FixMatch algorithm to automate classroom observation. An adaptation of the FixMatch algorithm was proposed to automate the coding for the Stallings class observation system. The proposed system was trained on classroom audio data collected in the wild. The supervised approach had an F1-score of 0.83 on 100% labeled data. The proposed FixMatch adaptation achieved an impressive F1-score of 0.81 on 20% labeled data, 0.79 on 15% labeled data, 0.76 on 10% labeled data, and 0.72 using only 5% of labeled data. This suggests that algorithms like FixMatch that use consistency regularization and pseudo-labeling have a great potential for being used to automate classroom observation using a small labelled set of audio snippets.
- Published
- 2022
19. Cigarette paper as evidence: Forensic profiling using ATR-FTIR spectroscopy and machine learning algorithms.
- Author
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Kapoor, Muskaan, Sharma, Akanksha, and Sharma, Vishal
- Subjects
- *
CIGARETTES , *FORENSIC sciences , *FOURIER transform infrared spectroscopy , *MACHINE learning , *ALGORITHMS - Abstract
This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations. [Display omitted] • Forensic evaluation of cigarette paper utilizing ATR-FTIR spectroscopy and Machine learning algorithms. • Peak characterization and differentiation-distinguishing cigarette paper from other types. • Machine learning algorithm comparison: assessing discrimination across nine cigarette brands. • External validation of the dominant algorithm using unknown samples. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.
- Author
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Alohali, Yousef A., Fayed, Mahmoud S., Mesallam, Tamer, Abdelsamad, Yassin, Almuhawas, Fida, and Hagr, Abdulrahman
- Subjects
DECISION trees ,SERIAL publications ,NATURAL language processing ,BIBLIOMETRICS ,MACHINE learning ,REGRESSION analysis ,RANDOM forest algorithms ,CITATION analysis ,DESCRIPTIVE statistics ,PREDICTION models ,ARTIFICIAL neural networks ,MEDICAL research ,MEDICAL specialties & specialists ,ALGORITHMS - Abstract
One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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21. Optimizing Parameters for Accurate Position Data Mining in Diverse Classrooms Layouts
- Author
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Shou, Tianze, Borchers, Conrad, Karumbaiah, Shamya, and Aleven, Vincent
- Abstract
Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain duration to establish a stop, this study presents a reproducible procedure to optimize a range parameter for K-12 classrooms where students sitting within a certain vicinity of an inferred stop are tagged as being visited. This extension is motivated by adapting parameters to infer teacher visits (i.e., on-task and off-task conversations between the teacher and one or more students) in an intelligent tutoring system classroom with a dense layout. We evaluate the accuracy of our algorithm and highlight a tradeoff between precision and recall in teacher visit detection, which favors recall. We recommend that future research adjust their parameter search based on stop detection precision thresholds. This adjustment led to better cross-validation accuracy than maximizing parameters for an average of precision and recall (F1 = 0.18 compared to 0.09). As stop sample size shrinks with higher precision cutoffs, thresholds can be informed by ensuring sufficient statistical power in offline analyses. We share avenues for future research to refine our procedure further. Detecting teacher visits may benefit from additional spatial features (e.g., teacher movement trajectory) and can facilitate studying the interplay of teacher behavior and student learning. [For the complete proceedings, see ED630829.]
- Published
- 2023
22. Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency
- Author
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Kim, Yunsung, Sreechan, Piech, Chris, and Thille, Candace
- Abstract
Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that scale poorly to massive datasets. In this work, we propose Variational Temporal IRT (VTIRT) for fast and accurate inference of dynamic learner proficiency. VTIRT offers orders of magnitude speedup in inference runtime while still providing accurate inference. Moreover, the proposed algorithm is intrinsically interpretable by virtue of its modular design. When applied to 9 real student datasets, VTIRT consistently yields improvements in predicting future learner performance over other learner proficiency models. [For the complete proceedings, see ED630829.]
- Published
- 2023
23. Clustering to Define Interview Participants for Analyzing Student Feedback: A Case of Legends of Learning
- Author
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Karimov, Ayaz, Saarela, Mirka, and Kärkkäinen, Tommi
- Abstract
Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played educational games within a week on the educational games platform, Legends of Learning and after a week, we asked them to fulfill the feedback survey about their feelings on the use of this platform. To analyze the collected data from students, firstly, we prepared clusters and selected one prototype student closest to the centroid of each cluster to interview. Interviews were held to explain the clusters more and due to time and resource limitations, we were unable to interview all (N=60) students, thus only the most representative students were interviewed. In addition to the students, we conducted an interview with the teacher as well to get her detailed feedback and observations on the usage of educational games. We also asked students to take an exam before and after the research to see the impact of games on their grades. Our results depict that though educational games can increase students' motivation, they may negatively impact some students' grades. And even though playing games made students feel interested and fun, they would not like to play them on a daily basis. Hence, using educational games for a certain duration such as subject revision weeks may positively influence students' grades and motivation. [For the complete proceedings, see ED630829.]
- Published
- 2023
24. Is Your Model 'MADD'? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models
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Verger, Mélina, Lallé, Sébastien, Bouchet, François, and Luengo, Vanda
- Abstract
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, so far, existing fairness metrics used in education are predictive performance-oriented, focusing on assessing biased outcomes across groups of students, without considering the behaviors of the models nor the severity of the biases in the outcomes. Therefore, we propose a novel metric, the Model Absolute Density Distance (MADD), to analyze models' discriminatory behaviors independently from their predictive performance. We also provide a complementary visualization-based analysis to enable fine-grained human assessment of how the models discriminate between groups of students. We evaluate our approach on the common task of predicting student success in online courses, using several common predictive classification models on an open educational dataset. We also compare our metric to the only predictive performance-oriented fairness metric developed in education, ABROCA. Results on this dataset show that: (1) fair predictive performance does not guarantee fair models' behaviors and thus fair outcomes; (2) there is no direct relationship between data bias and predictive performance bias nor discriminatory behaviors bias; and (3) trained on the same data, models exhibit different discriminatory behaviors, according to different sensitive features too. We thus recommend using the MADD on models that show satisfying predictive performance, to gain a finer-grained understanding on how they behave and regarding who and to refine models selection and their usage. Altogether, this work contributes to advancing the research on fair student models in education. Source code and data are in open access at https://github.com/melinaverger/MADD. [For the complete proceedings, see ED630829.]
- Published
- 2023
25. Proceedings of the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, July 11-14, 2023)
- Author
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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
26. Development and Preliminary Testing of the Algopaint Unplugged Computational Thinking Assessment for Preschool Education
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Zsoldos-Marchi?, Iuliana and Bálint-Svella, Éva
- Abstract
The concept, development and assessment of computational thinking have increasingly become the focus of research in recent years. Most of this type of research focuses on older children or adults. Preschool age is a sensitive period when many skills develop intensively, so the development of computational thinking skills can already begin at this age. The increased interest in this field requires the development of appropriate assessments. Currently, there are only a limited number of computational thinking assessments for preschool children. Based on this shortcoming, an assessment tool, named AlgoPaint Unplugged Computational Thinking Assessment for Preschool, was created addressed for 4-7 years old children. It is a paper-pencil-based test, which examines the following computational thinking domains: algorithms and debugging. Regarding computational concepts, simple instructions, simple and nested loops, and conditionals are included in the test. For the preliminary testing, AlgoPaint test was applied by 11 preschool teachers with 56 preschool age children. The test was also evaluated by 6 experts in algorithmic thinking working at universities. Based on the feedback given by the teachers and the experts, and the results of the children, AlgoPaint Computational Thinking Test was revised and completed. The revised version of the test is included in the appendix of the paper.
- Published
- 2023
27. Mining, Analyzing, and Modeling the Cognitive Strategies Students Use to Construct Higher Quality Causal Maps
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Allan Jeong and Hyoung Seok-Shin
- Abstract
The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the same processes produce higher-quality causal maps. This study analyzed the first five nodes that students (n = 37) placed in their causal maps to reveal that: 1) use of backward, forward, breadth-first, and depth-first processing produced maps of similar quality; and 2) backward processing had three times more impact on maps scores than depth-first processing to suggest that linking events into chains using backward chaining is one approach to constructing higher quality causal maps. These findings are compared with prior research findings and discussed in terms of noted differences in the task demands of constructing argument versus causal maps to gain insights into why, how, and when specific processes/strategies can be applied to create higher-quality causal maps and argument maps. These insights provide guidance on ways to develop diagramming and analytic tools that automate, analyze, and provide real-time support to improve the quality of students' maps, learning, understanding, and problem-solving skills. [For the full proceedings, see ED636095.]
- Published
- 2023
28. The Effects of Age and Learning with Educational Robotic Devices on Children's Algorithmic Thinking
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Angeli, Charoula, Diakou, Panayiota, and Anastasiou, Vaso
- Abstract
Educational Robotics is increasingly used in elementary-school classrooms to develop students' algorithmic thinking and programming skills. However, most research appears descriptive and lacks experimental evidence on the effects of teaching interventions using robotics to develop algorithmic thinking. Using the robots Dash and Dot, this study examined algorithmic thinking development in groups of children aged 6, 9, and 12. The results showed a statistically significant main effect between the age of students and algorithmic thinking skills and a statistically significant main effect between intervention and algorithmic thinking. In conclusion, the findings underscore the necessity of providing learners with structured, scaffolded activities tailored to their age to effectively nurture algorithmic thinking skills when engaging in Dash and Dot activities. [For the full proceedings, see ED636095.]
- Published
- 2023
29. Changing the Success Probability in Computerized Adaptive Testing: A Monte-Carlo Simultion on the Open Matrices Item Bank
- Author
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Hanif Akhtar
- Abstract
For efficiency, Computerized Adaptive Test (CAT) algorithm selects items with the maximum information, typically with a 50% probability of being answered correctly. However, examinees may not be satisfied if they only correctly answer 50% of the items. Researchers discovered that changing the item selection algorithms to choose easier items (i.e., success probability > 50%), albeit not optimum from a measurement efficiency standpoint, would provide a better experience. The current study aims to investigate the impact of changing the success probability on measurement efficiency. A Monte-Carlo simulation was performed on the Open Matrices Item Bank and simulated item bank. A total of 1500 examinees were generated. We modified the item selection algorithm with the expected success probability of 60%, 70%, and 80%. Each examinee was assigned to five item selection methods: maximum-information, random, p=0.6, p=0.7, and p=0.8. The results indicated that traditional CAT was 60-70% shorter than random item selection. Altering the success probability did not affect the estimation of the examinee's ability. Increasing the probability of success in CAT increased the number of items required to achieve specified levels of precision. Practical considerations on how to maximize the trade-off between examinees' experiences and measurement efficiency are mentioned in the discussion. [For the full proceedings, see ED654100.]
- Published
- 2023
30. The Relationship between Knowledge Production and Google in Framing and Reframing AI Imaginary. A Comparative Algorithmic Audit between the US and Italy
- Author
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Natalia Stanusch
- Abstract
This study offers an analysis and comparison of search results from Google concerning the topic of Artificial Intelligence (AI) in two geographically and politically different contexts: the United States and Italy. As new AI systems, tools, and solutions are developed and implemented in each sector of human life on a global scale, certain imaginaries of AI are emerging. These imaginaries constitute the ground for the public understanding, support, and disapproval of certain AI technologies and regulations. As citizens turn into users, Google remains the dominant gatekeeper of information, thus becoming an influential actor in sharping AI imaginaries. The following analysis is a response to the criticism of Google's search results, considering Google as an essential producer and certifier of AI imaginaries for general public. The comparison of search queries conducted in this analysis shows that the sources which Google presents in its search results add to different types of AI imaginaries, consequently influencing public opinion in different, often asymmetrical, ways. [For the full proceedings, see ED654100.]
- Published
- 2023
31. VIBRANT-WALK: An algorithm to detect plagiarism of figures in academic papers.
- Author
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Parmar, Shashank and Jain, Bhavya
- Subjects
- *
PLAGIARISM , *COMPUTER algorithms , *ALGORITHMS , *COMPUTER vision , *RANDOM walks - Abstract
Detecting plagiarism in academic papers is crucial for maintaining academic integrity, preserving the originality of published work, and safeguarding intellectual property. While existing applications excel at text plagiarism detection, they fall short when it comes to image plagiarism. This paper introduces a novel algorithm, named "VIBRANT-WALK," designed to detect image plagiarism in academic manuscripts. The challenge of identifying plagiarized images is formidable, requiring a unique approach. Traditional Computer Vision algorithms, proficient in image similarity tasks, face limitations in determining whether an image has been previously used in an article. To address this, the proposed algorithm leverages a repository of all published article pages, focusing on absolute identicality rather than image similarity. The algorithm comprises two stages. In the first stage, a "Vibrancy Matrix" is created through image preprocessing, aiding in contour determination. The second stage involves pixel-by-pixel comparison with images from published manuscripts. To enhance efficiency, the algorithm initiates comparisons from the pixel with the highest score in the Vibrancy Matrix, followed by pixel comparisons through random walks, significantly reducing complexity. To conduct the study, a custom dataset was compiled from 69 research articles, capturing snapshots of each page and figure. Overall, we present 485 unique test cases where we can test the accuracy and efficiency of the algorithm. The lack of publicly available datasets necessitated this approach. The proposed algorithm outperformed the existing models and algorithms in this field by achieving an overall accuracy of 94.8% on the collated dataset, identifying 460 instances of plagiarism out of the 485 test cases. The algorithm also demonstrated a 100% accuracy rate in avoiding false positives. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
32. A reviewer-reputation ranking algorithm to identify high-quality papers during the review process.
- Author
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Gao, Fujuan, Fenoaltea, Enrico Maria, Zhang, Pan, and Zeng, An
- Subjects
- *
ALGORITHMS , *CITATION networks , *REPUTATION , *RESEARCH personnel , *BIPARTITE graphs , *BEES algorithm - Abstract
With the exponential growth in the number of academic researchers, it is crucial for editors of scientific journals to identify the highest-quality papers. While several measures exist to evaluate a paper's impact post-publication, the challenge of determining the potential impact of a manuscript during the review process remains an understudied issue. In this paper, we propose a reviewer-reputation ranking algorithm to identify high-quality papers based on paper citations, where a reviewer's reputation is computed from the correlation between their past ratings and the current number of citations received by the papers they have evaluated. During the review process, reviewers with high reputation scores are given more weight to determine the quality of papers. We test the algorithm on an artificial network with 200 reviewers and 600 papers, as well as on the American Physical Society (APS) data set, including in the analysis 308,243 papers and 274,154 mutual citations. We compare our approach with two existing methods, demonstrating that our algorithm significantly outperforms the others in identifying manuscripts with the highest quality. Our findings can help improve the impact of scientific journals, thereby contributing to academic and scientific progress. • We propose an algorithm to identify the papers with the highest quality from a large number of submissions. • We compare our new algorithm with other existing methods of aggregating user ratings in various online services. • We test our algorithm both with an artificial network and with the empirical data of the APS data set. • We show that our algorithm outperforms the other methods in identifying the papers with the highest quality. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
33. S-integral points on elliptic curves - Notes on a paper of B. M. M. de Weger
- Author
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HERRMANN, Emanuel and PETHŐ, Attila
- Published
- 2001
34. A Paper-and-Pencil gcd Algorithm for Gaussian Integers
- Author
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Szabó, Sándor
- Published
- 2005
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35. The Role of Artificial Intelligence in English Language and Literature Reading Management
- Author
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Xisheng Chen
- Abstract
Firstly, this paper analyzes the role of AI in the reading management of English language and literature, establishes the implicit knowledge base of neural network, designs the auxiliary reading system for learning English language and literature, and optimizes the English language and literature management model of AI. The experimental results show that its reading efficiency is increased by 0.48%, and the performance of the credibility model is improved by 0.53% compared with the original system, which greatly optimizes the running time of the system. To some extent, it helps users to manage their time in English language and literature reading, and greatly improves users' reading efficiency and quality. Based on this advantage of AI algorithm, this paper introduces that the algorithm optimizes the reading management model and the training process of neural grid, and constructs a model of English language and literature assisted reading system based on AI. The system can better meet the needs of users in English language and literature reading management.
- Published
- 2024
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- View/download PDF
36. An Operations Research-Based Teaching Unit for Grade 11: The ROAR Experience, Part II
- Author
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Gabriella Colajanni, Alessandro Gobbi, Marinella Picchi, Alice Raffaele, and Eugenia Taranto
- Abstract
In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.
- Published
- 2024
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37. Capability Assessment of Cultivating Innovative Talents for Higher Schools Based on Machine Learning
- Author
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Rongjie Huang, Yusheng Sun, Zhifeng Zhang, Bo Wang, Junxia Ma, and Yangyang Chu
- Abstract
The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.
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- 2024
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38. Law Case Teaching Combining Big Data Environment with SPSS Statistics
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Zhao Wang
- Abstract
This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.
- Published
- 2024
- Full Text
- View/download PDF
39. Analysis of Piano Online Teaching System Based on Maximum Logarithm MPA Algorithm Technology
- Author
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Jing Shi, Na Wan, and Roslina Ibrahim
- Abstract
The application of computer technology has revolutionized and promoted the traditional mode of piano teaching. Nowadays, many companies and institutions have begun to apply computer technology to online piano teaching. This paper analyzes the difficulties faced by students in piano teaching and the development of piano assistant practice and summarizes the demands of parents, teachers, students, and principals for online piano teaching system. Based on this, this paper designs and implements an online piano teaching system without special hardware. This system improves the existing maximum logarithm MPa algorithm and improves the detection performance while keeping the complexity low. Combined with the special structure of parallel projection, a generalized automaton model of hybrid system is proposed, and five elements are used to describe the continuous and discrete behaviors in the hybrid subsystem. It not only keeps the advantage of low complexity of the original Max log MPa algorithm, but also obtains better detection performance.
- Published
- 2024
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- View/download PDF
40. Evaluation Model of Modern Network Teaching Quality Based on Artificial Intelligence E-Learning
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Hongyu Xie, He Xiao, and Yu Hao
- Abstract
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society.
- Published
- 2024
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- View/download PDF
41. Strategies of Infiltrating Psychological Fitness Education into Ideological and Political Education
- Author
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Ma Yin and Xiangang Hu
- Abstract
As the cradle of cultivating talents, universities are facing great opportunities and challenges in their education. Among them, IPE (ideological and political education), as an important foundation for the future growth of university students, is of great significance. This paper discusses the relationship between IPE and psychological fitness education in university teaching. This paper expounds the necessity and feasibility of playing the role of psychological fitness education in IPECU (ideological and political education in colleges and universities). Based on this, this paper gives the strategy of infiltrating psychological fitness education into IPE. This paper combines NN (neural network) method to construct an assessment model of IPE quality. In this paper, MATLAB is used for simulation and comparative analysis. The final experiment shows that the RMSE of this algorithm is 0.512, MAE is 1.089, and the accuracy of the algorithm is 0.958.
- Published
- 2024
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42. PROBLEMATIC ISSUES OF H-INDEX CAPTURING – HOW TO WRITE PAPERS AND MAKE LIFE EASIER FOR THE ALGORITHMS.
- Author
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Tumanishvili, George G.
- Subjects
AUTHOR-publisher relations ,TRACKING algorithms ,ALGORITHMS ,PERIODICAL publishing ,ENGLISH language - Abstract
Differentiation/ranking of authors in the scientific sphere is carried out according to their influence on the particular field(s) of science. The impact of the contribution is measured by the impact a text has on the development of the field/issue. It can be measured by the usage of ideas from other researchers works given as citations. Nowadays, citations are tracked by specific algorithms and citation management systems that have access to various databases, catalogues and bibliography systems through metadata. In the presented article, I discuss problematic issues that authors are facing while writing texts in different languages (other than the English language) and publishing them in periodicals. The most popular (often indicative) texts/authors still fail to be captured/cached by the algorithms, therefore creating an imbalance between the actual number of citations performed by other scholars and cached h-index displayed by the algorithm. The paper discusses the causes of the problem and suggests solutions for both authors and publishers. [ABSTRACT FROM AUTHOR]
- Published
- 2020
43. An Approach to Automatic Reconstruction of Apictorial Hand Torn Paper Document.
- Author
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Lotus, Rayappan, Varghese, Justin, and Saudia, Subash
- Subjects
AUTOMATION ,PAPER ,ARCHAEOLOGY ,FORENSIC sciences ,ALGORITHMS - Abstract
Digital automation in reconstruction of apictorial hand torn paper document increases efficacy and reduces human effort. Reconstruction of torn document has importance in various fields like archaeology, art conservation and forensic sciences. The devised novel technique for hand torn paper document, consists of pre-processing, feature extraction and reconstruction phase. Torn fragment's boundaries are simplified as polygons using douglas peucker polyline simplification algorithm. Features such as Euclidean distance and number of sudden changes in contour orientation are extracted. Our matching criteria identify the matching counterparts. Proposed features curtail ambiguity and enriches efficacy in reconstruction. Reconstructed results of hand torn paper document favour the proposed methodology. [ABSTRACT FROM AUTHOR]
- Published
- 2016
44. Development and Validation of an Algorithm for the Digitization of ECG Paper Images.
- Author
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Randazzo, Vincenzo, Puleo, Edoardo, Paviglianiti, Annunziata, Vallan, Alberto, and Pasero, Eros
- Subjects
- *
DIGITIZATION , *DIGITAL images , *ELECTROCARDIOGRAPHY , *HEART rate monitors , *PEARSON correlation (Statistics) , *MEASUREMENT errors , *HEART beat , *ALGORITHMS - Abstract
The electrocardiogram (ECG) signal describes the heart's electrical activity, allowing it to detect several health conditions, including cardiac system abnormalities and dysfunctions. Nowadays, most patient medical records are still paper-based, especially those made in past decades. The importance of collecting digitized ECGs is twofold: firstly, all medical applications can be easily implemented with an engineering approach if the ECGs are treated as signals; secondly, paper ECGs can deteriorate over time, therefore a correct evaluation of the patient's clinical evolution is not always guaranteed. The goal of this paper is the realization of an automatic conversion algorithm from paper-based ECGs (images) to digital ECG signals. The algorithm involves a digitization process tested on an image set of 16 subjects, also with pathologies. The quantitative analysis of the digitization method is carried out by evaluating the repeatability and reproducibility of the algorithm. The digitization accuracy is evaluated both on the entire signal and on six ECG time parameters (R-R peak distance, QRS complex duration, QT interval, PQ interval, P-wave duration, and heart rate). Results demonstrate the algorithm efficiency has an average Pearson correlation coefficient of 0.94 and measurement errors of the ECG time parameters are always less than 1 mm. Due to the promising experimental results, the algorithm could be embedded into a graphical interface, becoming a measurement and collection tool for cardiologists. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill.
- Author
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Zhang, Huanhuan, Li, Jigeng, Hong, Mengna, Man, Yi, and He, Zhenglei
- Subjects
PAPER mills ,FLOW shop scheduling ,PRODUCTION scheduling ,INDUSTRIAL costs ,ALGORITHMS - Abstract
With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. Are We Facing an Algorithmic Renaissance or Apocalypse? Generative AI, ChatBots, and Emerging Human-Machine Interaction in the Educational Landscape
- Author
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Aras Bozkurt and Ramesh C. Sharma
- Abstract
This study explores the transformative potential of Generative AI (GenAI) and ChatBots in educational interaction, communication, and the broader implications of human-GenAI collaboration. By examining the related literature through data mining and analytical methods, the paper identifies three main research themes: the revolutionary role of GenAI-powered ChatBots in educational interactions, their capability to enrich social learning, and their dual role as both support and assistance within educational settings. This research further highlights the impact of human-GenAI interaction in education from social, psychological, and cultural perspectives, focusing on social presence as a fundamental component of the teaching and learning process. It discusses the integration of GenAI and ChatBots into education and considers whether this marks the dawn of an algorithmic renaissance that elevates educational experiences or an apocalypse that threatens the very essence of human learning and interaction.
- Published
- 2024
47. A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric
- Author
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Melina Verger, Chunyang Fan, Sébastien Lallé, François Bouchet, and Vanda Luengo
- Abstract
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.
- Published
- 2024
48. A Course Recommender System Built on Success to Support Students at Risk in Higher Education
- Author
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Kerstin Wagner, Agathe Merceron, Petra Sauer, and Niels Pinkwart
- Abstract
In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.
- Published
- 2024
49. Automated Summary Scoring with Readerbench
- Author
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Botarleanu, Robert-Mihai, Dascalu, Mihai, Allen, Laura K., Crossley, Scott Andrew, and McNamara, Danielle S.
- Abstract
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale. Machine learning models leveraging Natural Language Processing (NLP) techniques were trained to predict the extent to which summaries capture the main idea of the target text. The NLP models combined both domain and language independent textual complexity indices from the ReaderBench framework, as well as state-of-the-art language models and deep learning architectures to provide semantic contextualization. The models achieve low errors -- normalized MAE ranging from 0.13-0.17 with corresponding R2 values of up to 0.46. Our approach consistently outperforms baselines that use TF-IDF vectors and linear models, as well as Transfomer-based regression using BERT. These results indicate that NLP algorithms that combine linguistic and semantic indices are accurate and robust, while ensuring generalizability to a wide array of topics. [This paper was published in: A. I. Cristea and C. Troussas (Eds.), "ITS 2021: Intelligent Tutoring Systems proceedings," pp. 321-332, 2021. Springer, Cham Switzerland.]
- Published
- 2021
- Full Text
- View/download PDF
50. Developments in the Design of Experiments, Correspondent Paper
- Author
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Atkinson, A. C.
- Published
- 1982
- Full Text
- View/download PDF
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