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Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.

A shift in primacy from online participatory cultures to algorithmic cultures invites new questions about literacies in digital contexts. This article contributes to the conceptualisation of literacies in algorithmic cultures through sociomaterial and affect theories. It develops a sociomaterial perspective that proposes felt, observable moments of user-algorithmic co-productions of culture as a needed unit of analysis for researching contemporary, critical digital literacies. It then employs this unit as a starting point for analysing the interplay of feeling, critical reflection and algorithm agency across one young adult's self-described literacy practice of 'working algorithms' across social media platforms. Analysis illustrates how critical literacies in algorithmic cultures are driven by processes of human-machine feeling-thinking that cannot be reduced to rational critiques of ideologies, platform capitalism or other forms of power alone. It describes how Malaya became more attuned over time to the affects of working with platform algorithms to craft her community, her sense of self and her sense of well-being. This sensitivity to feeling moved and feeling the capacity to move machines through the use of her literacies highlights how the facilitation of affect is a crucial point of analysis in understanding contemporary digital literacies.

Algorithmic systems such as Learning Analytics (LA) are driving the datafication and algorithmization of education. In this research, we focus on the appropriateness of LA systems from the perspective of parents and students in secondary education. Anchored in the contextual integrity framework (Nissenbaum, "Washington Law Review, 79," 41, 2004), we conducted two survey studies (N[subscript students]=277, N[subscript parents]=1013) in Flanders to investigate how they evaluate the appropriateness of the data flows in LA systems, and how both populations differ in their evaluations. The results show that the most-used student-centered LA are perceived less appropriate than the less-used teacher-centered LA by both students and parents. The usage of personal characteristics in LA is perceived as least appropriate, in contrast to coarser class characteristics. Sharing insights of LA with institutions that are part of the traditional educational context, such as the school, is seen as the most appropriate, and more appropriate than sharing it with learning platforms or third parties (e.g., Big Tech). Overall, we found that parents evaluated the different elements of the dataflows embedded in LA as less appropriate than students. In the discussion, we argue that educational institutions should include the evaluation of both parents and students to further manage expectations and construct shared norms and practices when implementing LA in education.informa

Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their ability to predict future academic success accurately. To address this, we propose FeatureX, a hybrid approach aiming to select high-performing features that impact student quality and reduce dropout rates. FeatureX integrates filter-based and wrapper-based methods to identify relevant features for predicting student performance. This approach enhances educational experiences by optimizing resource allocation and support services. Additionally, the Confidence-Weighted Fusion Voting Classifier (CWFVC) Algorithm supplements feature selection with performance forecasting capabilities, improving accuracy by combining diverse machine learning classifiers. The research evaluates FeatureX using Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Performance metrics, including accuracy, precision, recall, and F1-score, measure FeatureX's effectiveness. Results show that FeatureX achieves the highest accuracy with a subset of ten features, effectively identifying influential predictors. The CWFVC Algorithm further enhances performance forecasting accuracy, enabling timely identification of at-risk students and reducing dropout rates to foster inclusive education. Our research addresses the demand for data-driven approaches in education, offering an innovative method for predicting student performance and enhancing educational outcomes for diverse students. FeatureX and the CWFVC Algorithm provide valuable tools for educators and administrators to optimize resources, tailor support services, and create a more inclusive learning environment. Leveraging EDM and performance forecasting, educational institutions can proactively support students and promote academic success, contributing to an equitable and effective educational system.

Literature screening is the process of identifying all relevant records from a pool of candidate paper records in systematic review, meta-analysis, and other research synthesis tasks. This process is time consuming, expensive, and prone to human error. Screening prioritization methods attempt to help reviewers identify most relevant records while only screening a proportion of candidate records with high priority. In previous studies, screening prioritization is often referred to as automatic literature screening or automatic literature identification. Numerous screening prioritization methods have been proposed in recent years. However, there is a lack of screening prioritization methods with reliable performance. Our objective is to develop a screening prioritization algorithm with reliable performance for practical use, for example, an algorithm that guarantees an 80% chance of identifying at least 80% of the relevant records. Based on a target-based method proposed in Cormack and Grossman, we propose a screening prioritization algorithm using sampling with replacement. The algorithm is a wrapper algorithm that can work with any current screening prioritization algorithm to guarantee the performance. We prove, with mathematics and probability theory, that the algorithm guarantees the performance. We also run numeric experiments to test the performance of our algorithm when applied in practice. The numeric experiment results show this algorithm achieve reliable performance under different circumstances. The proposed screening prioritization algorithm can be reliably used in real world research synthesis tasks.

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.

The authors have found that having students learn accessible standard algorithms by explaining them using mathematics drawings increases students' sense of place--value numbers and enables students to articulate their understanding of what is actually happening with the numbers and why. In this article, they will discuss three standard algorithms for multidigit addition: the common one and two options that they have found to be more accessible to students. The accessible standard algorithms that they will discuss were each originally invented by students with whom they were working. They were then successfully introduced to many other students by teachers in classroom settings from all parts of the U.S., ranging from high to low poverty, and including English speakers and multilingual learners.

With the increased sophistication of technology, humans have the possibility to offload a variety of tasks to algorithms. Here, we investigated whether the extent to which people are willing to offload an attentionally demanding task to an algorithm is modulated by the availability of a bonus task and by the knowledge about the algorithm's capacity. Participants performed a multiple object tracking (MOT) task which required them to visually track targets on a screen. Participants could offload an unlimited number of targets to a "computer partner". If participants decided to offload the entire task to the computer, they could instead perform a bonus task which resulted in additional financial gain - however, this gain was conditional on a high performance accuracy in the MOT task. Thus, participants should only offload the entire task if they trusted the computer to perform accurately. We found that participants were significantly more willing to completely offload the task if they were informed beforehand that the computer's accuracy was flawless (Experiment 1 vs. 2). Participants' offloading behavior was not significantly affected by whether the bonus task was incentivized or not (Experiment 2 vs. 3). These results combined with those from our previous study (Wahn et al. in PLoS ONE 18:e0286102, 2023), which did not include a bonus task but was identical otherwise, show that the human willingness to offload an attentionally demanding task to an algorithm is considerably boosted by the availability of a bonus task - even if not incentivized - and by the knowledge about the algorithm's capacity.

Technology Enabled Learning (TEL) has a major impact on the learning adaptability of the learners. During the COVID-19 pandemic, there has been a drastic change in the learning methodology. The adaptability of learners from the various domains, levels and age has been a significant component of research in context to education. In this paper, the authors have proposed a machine learning and explainable AI based solution to identify critical learning parameters for students' adaptability level in online education. In this research the authors have employed various explainable AI (XAI) algorithms namely Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), FEature iMportance based eXplanable AI algorithm (FAMeX) for identifying the critical learning parameters to decide the adaptability level of a student. To test the efficacy of the solution, a dataset of students of several education levels of Bangladesh, collected from both online and offline surveys has been used. The results revealed are quite interesting, and counter intuitive.

Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In this research, we confirm the possibilities of classifying students' learning behavior using data obtained from multimodal distribution. We employed computer algorithms to classify students' learning behavior in animated programming classrooms and used information from this classification to predict learning outcomes. Specifically, our study indicates the presence of three clusters of students in the domain of "stay active", "stay passive", and "to-passive". We also found a relationship between these profiles and learning outcomes. We discussed our findings in accordance with the engagement and instructional quality models and believed that our statistical approach will support the ongoing refinement of the models in the context of behavioral profiling and classroom interaction. We recommend that further studies should identify different epistemological frames in diverse classroom settings to provide sufficient explanations of students' learning processes.

The zonation system is one of the efforts of the Indonesian Government to improve the quality of education services continuously, but the implementation is still considered less useful. Some areas were still grouped into the different zone from the original. Based on this problem, the solution needed for the zonation mapping of each school so that the apparent zonation limit can be resolved. An application that can map schools in an area based on housing for prospective students is a solution that can be considered at this time. This research showed the implementation of Voronoi diagrams to cluster the area based on the school position within Bengkulu City. The system was also capable of mapping the residential of the student address to match the choice for registration in available secondary school and senior high school. The implementation of the system showed a better result for school zonation over Bengkulu City based on web geographic information system (Web GIS). Furthermore, the comparison analysis over the existing system also showed that the performance of the Voronoi diagram for school zonation the method could solve the problem of the overlapping zonation with Radius method. Three hundred fifty students validated this system to show the performance of this method. The result showed that 72.05% of students matched the first choice, while 27.95% of students matched the second choice. This research showed the analysis of the value of this gap appeared due to the student preferences and school capacity factors.

Algorithms and machine learning models are being used more frequently in educational settings, but there are concerns that they may discriminate against certain groups. While there is some research on algorithmic fairness, there are two main issues with the current research. Firstly, it often focuses on gender and race and ignores other groups. Secondly, studies often find algorithmic bias in educational models but don't explore ways to reduce it. This study evaluates three drop-out prediction models used in an online learning platform to teach German spelling skills. The aim is to assess the fairness of the models for (in part) less-studied demographic groups, including first spoken language, home literacy environment, parental education background, and gender. To evaluate the models, four fairness metrics are used: predictive parity, equalized odds, predictive equality, and ABROCA. The study also examines ways to reduce algorithmic bias by analyzing the models at each stage of the machine learning process. The results show that all three models had biases that affected the fairness of all four demographic groups to varying degrees. However, the study found that most biases could be mitigated during the process. The methods used to mitigate bias differed by demographic group, and some methods improved fairness for one group but worsened it for others. Therefore, the study concludes that reducing algorithmic bias for lessstudied demographic groups is possible, but finding the right method for each algorithm and demographic group is crucial.

The quality of writing in a second language (L2) is one of the indicators of the level of proficiency for many college students to be eligible for departmental studies. Although certain software programs, such as Intelligent Essay Assessor or IntelliMetric, have been introduced to evaluate second-language writing quality, an overall assessment of writing proficiency is still largely achieved through trained human raters. The question that needs to be addressed today is whether generative artificial intelligence (AI) algorithms of large language models (LLMs) could facilitate and possibly replace human raters when it comes to the burdensome task of assessing student-written academic work. For this purpose, first-year college students (n=43) were given a paragraph writing task which was evaluated through the same writing criteria introduced to the generative pre-trained transformer, ChatGPT-3.5, and five human raters. The scores assigned by the five human raters revealed a statistically significant low to high positive correlation. A slight to fair but significant level of agreement was observed in the scores assigned by ChatGPT-3.5 and two of the human raters. The findings suggest that reliable results could be obtained when the scores of an application and multiple human raters are considered and that ChatGPT may potentially assist human raters in assessing L2 college writing.

The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data to predict their academic performance. In this study, this interaction information is called "week 0" data. The results suggest that "week 0" interaction data can be used to identify the academic success of online students and predict first assignment performance.

This study discusses the use of an online learning recommendation system as a smart solution related to changing the face-to-face learning process to online. This study uses user-based collaborative filtering, item-based collaborative filtering, and hybrid collaborative filtering. This research was conducted in two stages using the KNN machine learning algorithm: (1) the three methods were tested to obtain student grade prediction results without adding contextual information; and (2) with the same method the same steps were carried out but with the addition of contextual information features as a feature addition. One of the alternatives carried out in this study is related to the possibility of predicting student grades. This study proves that the use of contextual information as an additional feature in the recommendation system has a significant effect on the accuracy of student score prediction results, which are used as the basis for providing recommendations using the rule base technique.

In the context of a rapid digital transformation, digital competence is now regarded as a fourth cultural skill complementing reading, writing, and arithmetic. We argue that a well-structured and sound competence model is needed as a shared foundation for learning, teaching, pedagogical diagnostics and evaluative schemes in the school system. Every competence model should build upon a consistent, theoretically sound framework for teaching and learning. We consequently develop a competence model for digital competence by drawing on the concept of computational thinking as well as on general learning taxonomies. By combining different knowledge and process dimensions with essential facets of computational thinking a cube model of digital competence can be constructed. Hence, we develop and substantiate a structure model for digital competence building upon the concept of computational thinking that goes beyond the existing frameworks only focusing on the subject-related context and present this for discussion. The next step would then be to supplement the structure model with specific learning objectives, so that developing approaches to teaching and learning digital competence has a sound basis. [Note: The publication year (2022) shown on the PDF is incorrect. The correct publication year for this article is 2023.]

Purpose: Artificial intelligence (AI) chatbots, such as ChatGPT and GPT-4, developed by OpenAI, have the potential to revolutionize education. This study explores the potential benefits and challenges of using ChatGPT in education (or "educative AI"). Design/Approach/Methods: This paper proposes a theoretical framework called "IDEE" for educative AI such as using ChatGPT and other generative AI in education, which includes identifying the desired outcomes, determining the appropriate level of automation, ensuring ethical considerations, and evaluating effectiveness. Findings: The benefits of using ChatGPT in education or more generally, educative AI, include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers. However, challenges such as the untested effectiveness of the technology, limitations in the quality of data, and ethical and safety concerns must also be considered. Originality/Value: This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.

Generative AI is here to stay, and we need to explore the potential role of these technologies in distance education and online learning, considering both the benefits and challenges. With many potentials such as customized learning experiences, intelligent tutoring, automated grading, content creation, and personalized career advice, there are also a wide range of challenges such as bias in data and algorithms, lack of transparency, overreliance on AI, data privacy and security, access and equity, automation, and singularity. Based on these considerations, generative AI requires enhancing the scope of current educational roles or adopting new ones, such as facilitators of learning, curators of learning resources, designers of learning experiences, and assessors of learning. Though distance education and online learning are mostly attributed spatial and temporal distances, generative AI should also be considered in terms of transactional distance. While the use of generative AI has the potential to decrease the transactional distance, educators still can play a crucial role in bridging this gap by providing personalized support and guidance throughout the learning process not only in terms of pedagogical aspects, but also from the perspective of pedagogy of care and human-centered learning design.

This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.

The influence of algorithms on society is increasing due to their growing presence in all areas of daily life. Although we are not always aware of it, they sometimes usurp the identity of other social actors. The main purpose of this article is to address the meta-research on the field of artificial intelligence and communication from a holistic perspective that allows us to analyze the state of academic research, as well as the possible effects on these areas and on the democratic system. To this end, we carried out a systematized review of recent literature using quantitative and qualitative approaches. The subject analyzed is changing and novel: it includes the impact and interaction of algorithms, bots, automated processes, and artificial intelligence mechanisms in journalism and communication, as well as their effects on democracy. The results show expanding scientific production, mostly in English, based on theoretical discussion or focused on the perception of communication professionals. The object of study is centered mostly on journalism and democracy, and to a lesser degree on ethics or education. Studies indicate great interest in the effects of the use of algorithms on journalism and democracy, but the answers are still uncertain and the challenges for the coming years are significant.

Social media can contribute to an inclusive society, but they are also asymmetrical and polarised communication spaces. This requires competent teachers to build critical digital citizenship. The aim of this article is twofold: to present web scraping and text analytics as tools that define teachers' digital competences, and to investigate which posts on Twitter and Instagram are most viral in relation to education, disability and inclusion. A total of 48,991 publications in Spanish and English were analysed, corresponding to the period from 13 October 2021 to 1 May 2022. The 100 most viral posts were selected, and correlations were identified between the sentiment, gender and influence associated with the content, its temporal and geographic space. The results show that economic and political influence groups are the most viral, relegating non-profit organisations or individuals with altruistic outreach to second place; only on international days is this trend reversed. Bots do not interfere to impose messages; it is artificial intelligence algorithms that overshadow vindictive and humanistic content. The most influential people are predominantly male, associated with institutional accounts in the political sphere. It is concluded that Big Data and Business Intelligence tools help teachers to analyse relevant educational and social issues, and to acquire a collective ethic in the face of new educational challenges.

Social media have established a new way of communicating and understanding social relationships. At the same time, there are downsides, especially, their use of algorithms that have been built and developed under their umbrella and their potential to alter public opinion. This paper tries to analyse the YouTube recommendation system from the perspectives of reverse engineering and semantic mining. The first result is that, contrary to expectations, the issues do not tend to be extreme from the point of view of polarisation in all cases. Next, and through the study of the selected themes, the results do not offer a clear answer to the proposed hypotheses, since, as has been shown in similar works, the factors that shape the recommendation system are very diverse. In fact, results show that polarising content does not behave in the same way for all the topics analysed, which may indicate the existence of moderators --or corporate actions-- that alter the relationship between the variables. Another contribution is the confirmation that we are dealing with non-linear, but potentially systematic, processes. Nevertheless, the present work opens the door to further academic research on the topic to clarify the unknowns about the role of these algorithms in our societies.

The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational exercise has been successful and because collaboration has proven to be one of the most effective strategies to improve performance outcomes. This systematic review analyzes the relationship between AI, competency development, and collaborative learning (CL). PRISMA methodology is used with data from the SCOPUS database. A total of 1,233 articles were found, and 30 passed the inclusion and exclusion criteria. The analysis of the selected articles identified three categories that deserve attention: the objects of study, the way of analyzing the results, and the types of AI that could be used. In this way, it has been possible to determine the relationship offered by the studies between skill development and CL and ideas about AI's contributions to this field. Overall, however, the data from this systematic review suggest that, although AI has great potential to improve education, it should be approached with caution. More research is needed to fully understand its impact and how best to apply this technology in the classroom, minimizing its drawbacks, which may be relevant, and making truly effective and productive use of it.

Several studies compared three different types of questions (conceptual, algorithmic, and graphical) across various topics, however, few focused specifically on gifted students. This study addressed this gap. The aim of the study, hence, was to determine whether there were notable differences in gifted students' performance in the three types of tests. The study involved 115 gifted students aged between 17 and 18 years old. They responded to the three different tests including 10 test items in each with the same content. Significant differences were observed in their performances between the conceptual versus algorithmic in favour of the conceptual test, the conceptual versus graphical in favour of the conceptual test, and the algorithmic versus graphical in favour of the algorithmic test. Moreover, the statistical analysis results revealed that the students performed markedly poorer performance on the graphical test in comparison to both the algorithmic and conceptual tests. These results suggest the need to enhance students' graphical skills to facilitate a better understanding of physics concepts. Proper steps should be taken to improve their proficiency in interpreting and analysing graphical representations.

We live in a digital age, not least accelerated by the COVID-19 pandemic. It is all the more important in our society that students learn and master the key competence of algorithmic thinking to understand the informatics concepts behind every digital phenomena and thus is able to actively shape the future. For this to be successful, concepts must be identified that can convey this key competence to all students in such a way that algorithmic thinking is integrated in the subject of informatics -beyond a pure programming course. Furthermore, based on the Legitimation Code Theory, semantic waves provide a way to develop and review lesson plans. Therefore, we planned a workshop, that follow the phases of a semantic wave addressing algorithmic problems using a block-based programming language. Considering this, we suggest the so-called SWAT concept (Semantic Wave Algorithmic Thinking concept), which is carried out and analyzed in a workshop with students. The workshop was carried out in online format in an 8th grade of a high school during a coronavirus lockdown. The level of algorithmic thinking was measured using a pretest and posttest both in the treatment group and in a control group and with the help of the approximate adjusted fractional Bayes factors for testing informative hypotheses statistically and through a reductive, qualitative content analysis of the students' work results (worksheets and created programs) evaluated. The semantic wave concept was measured using several cognitive load ratings of the students during the workshop and also statistically evaluated with the approximate adjusted fractional Bayes factors for testing informative hypotheses, as well as a qualitative content analysis of the worksheets. Results of this pilot study provide first insights, that the SWAT-concept can be used in combination of unplugged and plugged parts.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.

The contents taught in the programming subjects have a great relevance in the formation of computing students. However, these subjects are characterized by high failure rates, as they require logical reasoning and mathematical knowledge. Thus, establishing knowledge through the subject of algorithms can help students to overcome these difficulties and absorb the contents and skills required. Thus, this work aims to present and discuss the results of a second experiment on the application of a teaching plan composed of several active methodologies (Virtual Learning Environments, Coding Dojo, Gamification, Problem-Based Learning, Flipped Classroom and Serious Games) in an algorithms subject. Based on this experiment, it was evaluated whether there were learning gains compared to the learning acquired with the traditional method. Finally, an analysis was performed using the two-tailed Student-t approach, used for independent samples, which presented statistically significant results.

This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a subset of data from 29,083 participants who submitted an assignment in one of nine entry-level MOOC courses. Manhattan distance and Gower distance measures are computed based on engagement, achievement, and demographic data. To our knowledge, this marks one of the first such uses of Gower distance to cluster mixed variable data to explore fairness and equity in the MOOC literature. The clusters are derived from CLARA and PAM algorithms, enriched by demographic data, with a particular focus on education level, as well as approximated socioeconomic status (SES) for a smaller subset of learners. Results indicate that learners without a college degree are more likely to be high-performing compared to college-educated learners. Learners from lower SES backgrounds are just as likely to be successful as learners from middle and higher SES backgrounds. While MOOCs have struggled to improve access to learning, more fair and equitable outcomes for traditionally underrepresented learners are possible.

Embedding a pairwise key distribution approach in IoT systems is challenging as IoT devices have limited resources, such as memory, processing power, and battery life. This paper presents a secure and lightweight approach that is applied to IoT devices that are divided into Voronoi clusters. This proposed algorithm comprises XOR and concatenation operations for interactive authentication between the server and the IoT devices. Predominantly, the authentication is carried out by the server. It is observed that the algorithm is resilient against man-in-the-middle attacks, forward secrecy, Denial of Service (DoS) attacks, and offers mutual authentication. It is also observed that the given scheme has low communication and computing overheads compared to some existing methods.

The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners a valuable lens through which to consider LA as a moral practice. This paper examines what data feminism can offer the LA community. It identifies critical questions for further developing and enabling a responsible stance in LA research and practice taking one particular case -- algorithmic decision-making -- as a point of departure.

Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The current study aimed to identify the effect of explanations of an automated essay scoring system on students' trust and motivation. The explanations were designed using a needs-elicitation study with students in combination with guidelines and frameworks of explainable AI. Two types of explanations were tested: full-text global explanations and an accuracy statement. The results showed that both explanations did not have an effect on student trust or motivation compared to no explanations. Interestingly, the grade provided by the system, and especially the difference between the student's self-estimated grade and the system grade, showed a large influence. Hence, it is important to consider the effects of the outcome of the system (here: grade) when considering the effect of explanations of AI in education.

In this study, we used multivariate decoding methods to study processing differences between canonical (montring and count) and noncanonical finger numeral configurations (FNCs). While previous research investigated these processing differences using behavioral and event-related potentials (ERP) methods, conventional univariate ERP analyses focus on specific time intervals and electrode sites and fail to capture broader scalp distribution and EEG frequency patterns. To address this issue a supervised learning classifier--support vector machines (SVM)--was used to decode ERP scalp distributions and alpha-band power for montring, counting, and noncanonical FNCs (for integers 1 to 4). The SVM was used to test whether the numerical information presented in FNCs can be decoded from the EEG data. Differences in the magnitude and timing of accuracy rates were used to compare the three types of FNCs. Overall, the algorithm was able to predict numerical information presented in FNCs beyond the random chance level accuracy, with higher rates for ERP scalp distributions than alpha-power. Montring had lower peak accuracy compared to counting and noncanonical configurations, likely due to automaticity in processing montring configurations leading to less distinct scalp distributions for the four numerical magnitudes (1 to 4). Paralleling the response time data, the peak decoding accuracy time for montring was earlier for montring (472 ms), compared to counting (577 ms) and noncanonical FNCs (604 ms). The results provide support for montring configurations being processed automatically, somewhat similar to number symbols, and provide additional insights for processing differences across different forms of FNCs. This study also highlights the strengths of decoding methods in EEG/ERP research on numerical cognition.

In our investigation of university students' knowledge about real numbers in relation to computer algebra systems (CAS) and how it could be developed in view of their future activity as teachers, we used a computer algorithm as a case to explore the relationship between CAS and the knowledge of real numbers as decimal representations. Our work was carried out in the context of a course for university students who aim to become mathematics teachers in high schools. The main data consists of students' written responses to an assignment of the course and interviews to clarify students' perspectives in relation to the responses. The analysis of students' work is based on the anthropological theory of the didactic (ATD). Our results indicate that simple CAS-routines have a potential to help university students (future teachers) to apply their university knowledge on certain problems related to the decimal representation of real number which are typically encountered but not well explained in high school.

A critical part of supporting the development of students' algorithmic thinking is understanding the challenges that emerge when students engage with algorithmatizing tasks--tasks that require the creation of an algorithm. Knowledge of these challenges can serve as a basis upon which educators can build effective strategies for enhancing students' algorithmic thinking skills. This paper presents three illustrative cases of emergent challenges evident as students grapple with the process of creating an algorithm. The first challenge highlights discrepancies between the method with which students solve a problem and the algorithm they create, and claim would, when implemented, solve the same problem. The second challenge pertains to the persistence of students' normatively incorrect algorithms, despite going through multiple iterations of testing and revising. Finally, the third challenge concerns issues around the use of test problems for supporting students in their creation of generalized algorithms. These three challenges are discussed using student data (illustrative cases) from three different mathematical algorithmatizing tasks. Suggestions are put forth for addressing some of these challenges, with a particular emphasis on practical pedagogical suggestions for cultivating students' mathematical thinking in the context of algorithmatizing tasks.

Governments and educational authorities around the world are emphasizing performance evaluation of educational systems. Opinion Mining (OM) has gained acceptance among experts in various regions, including the preparation space. The proposed model involves Two modules: the data preprocessing module and the opinion mining module. The main objective of our article is to enhance educational systems through the analysis of student comments, teacher comments and course comments. Furthermore, the proposed model uses a bundling task to make groups of packs for students from its comments. The datasets were 10,000 instances, 80% of which were for training and 20% for testing. The results showed that K-Means Algorithm had the best accuracy time /Sec of 0.03. The correctly classified 8,000 instances were equal to 96%, and incorrectly classified 2,000 instances were equal to 4%, Accuracy of the model is 95%, Recall is 94.8% and F-Measure is 93.7% between others algorithms. clustering and Association Rule Mining phases Algorithms namely Chi-Square test is good quality than Others Algorithms.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

Learning programming logic remains an obstacle for students from different academic fields. Considered one of the essential disciplines in the field of Science and Technology, it is vital to investigate the new tools or techniques used in the teaching and learning of Programming Language. This work presents a systematic literature review (SLR) on approaches using Mobile Learning methodology and the process of learning programming in introductory courses, including mobile applications and their evaluation and validation. We consulted three digital libraries, considering articles published from 2011 to 2022 related to Mobile Learning and Programming Learning. As a result, we found twelve mobile tools for learning or teaching programming logic. Most are free and used in universities. In addition, these tools positively affect the learning process, engagement, motivation, and retention, providing a better understanding, and improving content transmission.