ethanporcaro/eric-fulltext-meta huggingface dataset:
Meta-Learning for Better Learning: Using Meta-Learning Methods to Automatically Label Exam Questions with Detailed Learning Objectives
Author: Zur, Amir
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Equal Education,Prior Learning,Educational Objectives,Chemistry,Physics,Textbooks,Benchmarking,Science Instruction,Feedback (Response),Computer Assisted Testing,Classification,Networks,Computational Linguistics,Test Items

Detailed learning objectives foster an effective and equitable learning environment by clarifying what instructors expect students to learn, rather than requiring students to use prior knowledge to infer these expectations. When questions are labeled with relevant learning goals, students understand which skills are tested by those questions. Labeling also helps instructors provide personalized feedback based on the learning objectives each student struggles to master. However, developing detailed learning objectives is time-consuming, making many instructors unable to pursue it. Labeling course questions with learning objectives can be even more time-intensive. To address this challenge, we develop a benchmark for automatically labeling questions with learning objectives. The benchmark comprises 4,875 questions and 1,267 expert-verified learning objectives from college physics and chemistry textbooks. This dataset provides a large library of learning objectives, and, to the best of our knowledge, is the first benchmark to measure performance on labeling questions with learning objectives. We use meta-learning methods to train classifiers and test them against our benchmark in a few-shot classification setting. These classifiers achieve acceptable performance on a test set with previously unseen questions (AUC 0.84), as well as a course with previously unseen questions and unseen learning objectives (AUC 0.84). Our work facilitates labeling questions with learning objectives to help instructors provide better feedback and create equitable learning environments. [For the complete proceedings, see ED630829.]
Self-Assessment Task Processing Behavior of Students in Higher Education
Author: Kasakowskij, Regina
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Self Evaluation (Individuals),Student Behavior,College Students,Learning Processes,Computer Science Education,Feedback (Response),Behavior Patterns,Educational Technology,Technology Uses in Education

Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the student's learning process. So far, learning processes of students working on such Self-Assessment tasks have not been studied. Thus, SAFRAN was extended with activity logging allowing process mining. SAFRAN was used in a first-year computer science university course. Students' behavior was clustered and analyzed using log data. 3 task completion behavior patterns were identified indicating positive, neutral or negative impact on task processing. Differences in the use of feedback and sample solutions were also identified. The results are particularly relevant for instructors who can tailor adaptive feedback content better to its target group. The analytics approach described may be useful for researchers who want to implement and study adaptive and personalized task processing support. [For the complete proceedings, see ED630829.]
Semantic Topic Chains for Modeling Temporality of Themes in Online Student Discussion Forums
Author: Chopra, Harshita
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Computer Mediated Communication,Pandemics,COVID-19,Computer Software,Public Health,Course Content,Computational Linguistics,Health Services,Social Differences,Student Attitudes,Group Discussion,Discourse Analysis,Natural Language Processing,Learning Management Systems

Exploring students' discourse in academic settings over time can provide valuable insight into the evolution of learner engagement and participation in online learning. In this study, we propose an analytical framework to capture topics and the temporal progression of learner discourse. We employed a Contextualized Topic Modeling technique on messages posted by undergraduates in online discussion forums from Fall 2019 to Spring 2020. We further evaluated if topics were originating from specific courses or more generally distributed across multiple courses. Our results suggested a significant increase in the number of general topics after the onset of the pandemic, suggesting emergent topics being discussed in a range of courses. In addition, using Word Mover's Distance, we examined the semantic similarity of topics in adjacent months and constructed topic chains. Our findings indicated that previously course-centric topics such as public health developed into more general discussions that emphasize inequities and healthcare during the pandemic. Furthermore, emergent topics around students' lived experiences underscored the role of discussion forums in capturing educational experiences temporally. Finally, we discuss the implications of current findings for post-pandemic higher education and the effectiveness of our framework in exploring unstructured large-scale educational data. [For the complete proceedings, see ED630829.]
Session-Based Course Recommendation Frameworks Using Deep Learning
Author: Khan, Md Akib Zabed
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Course Selection (Students),Learning Analytics,Academic Advising,Decision Making,Higher Education,Academic Achievement,Educational Experience,Required Courses,Elective Courses,Models,Universities,Longitudinal Studies,Sequential Approach,Learning Management Systems,College Students,Computer Software,Preferences

Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to be explored is the suitability of the recommended courses taken together in a semester. Students may face more difficulty coping with the workload of courses if there is no relationship among courses taken within a semester. To address this problem, we propose to employ session-based approaches to recommend a set of courses for the next semester. In particular, we test two session-based recommendation models, CourseBEACON and CourseDREAM. Our experimental evaluation shows that session-based methods outperform existing popularity-based, sequential, and non-sequential recommendation approaches. Accurate course recommendation can lead to better student advising, which, in turn, can lead to better student performance, lower dropout rates, and better overall student experience and satisfaction. [For the complete proceedings, see ED630829.]
The Predictiveness of PFA Is Improved by Incorporating the Learner&apos;s Correct Response Time Fluctuation
Author: Chu, Wei
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Reaction Time,Accuracy,Models,Predictor Variables,Factor Analysis,Learning Management Systems,Learning Analytics,Psychological Patterns,Theories,Recall (Psychology),Probability,Learning Trajectories,Guessing (Tests),Learning Processes,Item Analysis,Item Response Theory,Comparative Analysis,Multiple Choice Tests,Chinese,Japanese,Language Tests,Pronunciation,English,Vocabulary,Cloze Procedure,Goodness of Fit

In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic regression for knowledge tracing has been used widely in modern learner performance modeling. Notably, the learning history included in such models is typically confined to learners' prior accuracy performance without paying attention to learners' response time (RT), such as the performance factors analysis (PFA) model. However, RT and accuracy may give us a more comprehensive picture of a learner's learning trajectory. For example, without considering RT, we cannot estimate whether the learner's performance has reached the automatic or fluent level since these criteria are not accuracy based. Therefore, in the current research, we propose and test new RT-related features to capture learners' correct RT fluctuations around their estimated ideal fluent RT. Our results indicate that the predictiveness of the standard PFA model can be increased by up to 10% for our test data after incorporating RT-related features, but the complexity of the question format constrains the improvement during practice. If the question is of low complexity and the observed accuracy of the learner can be influenced by guessing, which results in the imprecision measured by accuracy, then the RT-related features provide additional predictive power. In other words, RT-related features are informative when accuracy alone does not completely reflect learners' learning processes. [For the complete proceedings, see ED630829.]
Mixed-Method Analysis of Predisposing Factors Associated with Thesis-Writing Burnout amongst Chinese Postgraduates
Author: Wei, Li-Wei
Date: 2023-00-00
Type: Journal Articles
Subjects: Foreign Countries,Graduate Students,Theses,Writing (Composition),Burnout,Influences,Student Characteristics,Measures (Individuals)

This mixed-method analysis investigates thesis-writing burnout among 117 Chinese postgraduates, probing the patterns, magnitude, and influencing factors. A measurable burnout trend emerges across thesis-writing stages - Thesis-Writing Proposal (TTP), Thesis-Writing 3-Chapter (TW3C), and Thesis-Writing 5-Chapter (TW5C) - peaking (X=4.41) during TW3C, reflecting heightened exhaustion and cynicism. Notably, burnout varies with demographic attributes, specifically gender and marital status. Concurrently, exhaustion and cynicism levels fluctuate with thesis-writing progression, while professional efficacy gradually escalates. Participants convey a shift from profound exhaustion and cynicism in TTP and TW3C stages to resilience, manageable fatigue, and self-assuredness during TW5C. Predisposing factors encompass uncertainty, anxiety, and advisory attributes, spanning styles, diligence, and personality traits. These aspects, especially autocratic and laissez-faire approaches, exacerbate stress and impact motivation and research methods. These findings underscore the multifaceted nature of academic burnout during thesis-writing among Chinese postgraduates, necessitating stage-specific interventions. Such tailored strategies facilitate burnout prevention, promote thesis completion, and yield profound implications for Chinese conventional postgraduate education. The research also establishes the vital role of advisors in modulating stress levels, underscoring the need for proactive support measures and fostering a conducive academic environment. This study augments understanding of academic burnout, offering valuable insights to counteract its detrimental effects and bolster postgraduate students' academic journey.
The Right to Be Forgotten and Educational Data Mining: Challenges and Paths Forward
Author: Hutt, Stephen
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Decision Making,Data Collection,Foreign Countries,Legislation,Privacy,Learning Analytics,Barriers,Compliance (Legal),Models,Longitudinal Studies,Open Source Technology,Scientific Research

The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two decades, the EDM community has taken consistent steps to protect learner privacy within our research, whilst pursuing goals that will benefit their learning. However, recent privacy legislation may cause our practices to need to change. The right to be forgotten states that users have the right to request that all their data (including deidentified data generated by them) be removed. In this paper, we discuss the potential challenges of this legislation for EDM research, including impacts on Open Science practices, data modeling, and data sharing. We also consider changes to EDM best practices that may aid compliance with this new legislation. [For the complete proceedings, see ED630829.]
Investigating the Effects of Online Research Skills Instructions: A Quasi-Experimental Study in Grade 8
Author: Kain, Hoda
Date: 2020-00-00
Type: Journal Articles
Subjects: Foreign Countries,Research Skills,Skill Development,Intervention,Online Searching,Internet,Plagiarism,Grade 8,Middle School Students,Private Schools,Program Effectiveness,Student Attitudes

This article reports on a part of a larger mixed-methods, quasi-experimental study which investigated the effect of a comprehensive model of online source- use on developing online research skills and lessening copy-paste trend in the writing pieces of Grade 8 ESL students (n = 172) in three private schools in Saida, Lebanon. Eight teachers were randomly assigned into experimental and control conditions along with their respective classes. Five of these teachers implemented a ten--session intervention. The main researcher collected pre and post data on online research and comprehension assessment tool (revised-ORCA). Supported with current research and informed by the principles of the social- cognitive, socio-cultural, and the new literacies theories, statistical analyses revealed that the experimental group outperformed the control group, showing higher score gains from pre-test to post-test on an overall score of revised ORCA as well as on its subskills. It was also noted that the contribution of the intervention on the scores of communicate ideas responsibly was almost twice the effect on the rest of subskills. Theoretical and pedagogical implications generated from this study were discussed. Recommendations for school policies and further research were also provided.
To Speak or Not to Speak, and What to Speak, When Doing Task Actions Collaboratively
Author: Nasir, Jauwairia
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Cooperative Learning,Thinking Skills,Problem Solving,Learning Activities,Learning Processes,Content Analysis,Learning Analytics,Correlation,Teamwork,Concept Formation,Computer Assisted Instruction,Dialogs (Language),Computer Mediated Communication,Algorithms,Discourse Analysis,Foreign Countries

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.]
Predictors of Online Research Skills to Reduce Copy-Paste: An Intervention in Middle Schools
Author: Kain, Hoda
Date: 2023-00-00
Type: Journal Articles
Subjects: Middle School Students,Research Skills,Plagiarism,Online Searching,Internet,Grade 8,English (Second Language),Second Language Instruction,Private Schools,Intervention,Program Effectiveness,Student Behavior,Foreign Countries,Skill Development

The complexities of Internet sources have caused more challenges to school students, leading them in many cases to copy-paste ideas into their written assignments. Facing these challenges requires adopting and implementing evidence-based practices. This article builds on a mixed-methods, quasi-experimental study which investigated the effect of Internet Reciprocal Model (IRT) to lessen occurrences of copy-paste trend in the writing pieces of Grade 8 ESL students (n = 172) in three private schools in Saida, Lebanon. Eight teachers were randomly assigned to experimental and control conditions along with their respective classes. The researchers collected pre and post-data on online research and comprehension assessment tool. Supported with current research and informed by the principles of the social-cognitive, socio-cultural, and the new literacies theories, regression analyses revealed a strong positive degree of association at post-test between the intervention and the subscale that measured copy-paste trend, indicating that teaching online research skills through one comprehensive model would decrease the copy-paste behavior. Theoretical and pedagogical implications were discussed. Recommendations for school policies and further research were provided.
Unfolding Learners&apos; Response to Different Versions of Automated Feedback in a MOOC for Programming -- A Sequence Analysis Approach
Author: Gabbay, Hagit
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: MOOCs,Feedback (Response),Teaching Methods,Learning Strategies,Metacognition,Cognitive Processes,Student Motivation,Learning Analytics,Computer Assisted Testing,Assignments,Programming,Computer Science Education,Instructional Effectiveness,Open Source Technology

In MOOCs for programming, Automated Testing and Feedback (ATF) systems are frequently integrated, providing learners with immediate feedback on code assignments. The analysis of the large amounts of trace data collected by these systems may provide insights into learners' patterns of utilizing the automated feedback, which is crucial for the design of effective tools and maximizing their potential to promote learning. However, data-driven research on the impact of ATF on learning is scarce, especially in the context of MOOCs. In the current study, we combine a theoretical framework of feedback with educational data mining methods to investigate the effect of feedback characteristics on learning behavior in a MOOC. Sequence pattern analysis is implemented to explore and visualize the actions taken by learners in response to feedback which composed of cognitive, meta-cognitive, and motivational elements. We applied our research approach in an empirical design which consists of five cohorts (total over 2200 learners) utilizing different versions of ATF. The findings suggest that learners tend to adopt learning strategies in response to feedback and exhibit a preference for utilizing example solutions, while still coping with the challenge of solving the assignments independently. The impact of feedback function, content and structure is discussed in light of a detailed view of the differences as well as common trends in learning paths. Allowing for fine-grained insights, we found our research approach contributes to a more comprehensive understanding of the effect of automated feedback characteristics in MOOCs for programming. [For the complete proceedings, see ED630829.]
Using Markov Matrix to Analyze Students&apos; Strategies for Solving Parsons Puzzles
Author: Kumar, Amruth N.
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Markov Processes,Puzzles,Introductory Courses,Computer Science Education,Programming Languages,Teaching Methods,Student Characteristics,High School Students,Community College Students,Undergraduate Students,Intelligent Tutoring Systems,Computer Software

Is there a pattern in how students solve Parsons puzzles? Is there a difference between the puzzle-solving strategies of C++ and Java students? We used Markov transition matrix to answer these questions. We analyzed the solutions of introductory programming students solving Parsons puzzles involving if-else statements and while loops in C++ and Java from fall 2016 to fall 2020. We present the results of our analysis qualitatively as heat maps and quantitatively using descriptive statistics. We found that most students solved the puzzles in the order in which lines appeared in the correct solution. Counter-intuitively, we found this pattern even in the solutions of the puzzles involving nested if-else statements, multiple while loops and nested while loops. Students who solved the puzzles with the fewest actions acted upon fewer lines out of order, i.e., not in the order in which they appear in the final solution. Whenever we found a statistically significant difference between C++ and Java solutions, C++ solutions involved fewer out-of-order and redundant actions than Java solutions. We discuss the implications of these results for the use of Parsons puzzles as a tool for teaching introductory programming. [For the complete proceedings, see ED630829.]
Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning
Author: Vassoyan, Jean
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Reinforcement,Networks,Simulation,Educational Technology,Teaching Methods,Intelligent Tutoring Systems,Outcomes of Education,Educational Resources,Individualized Instruction,Graphs,Algorithms,Programming Languages

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.]