Title: Course-Adaptive Content Recommender for Course Authoring
Developing online courses is a complex and time-consuming process that involves organizing a course into a sequence of topics and allocating the appropriate learning content within each topic. This task is especially difficult in complex domains like programming, due to the incremental nature of programming knowledge, where new topics extensively build upon domain concepts that were introduced in earlier lessons. In this paper, we propose a course-adaptive content-based recommender system that assists course authors and instructors in selecting the most relevant learning material for each course topic. The recommender system adapts to the deep prerequisite structure of the course as envisioned by a specific instructor, while unobtrusively deducing that structure from problem-solving examples that the instructor uses to present course concepts. We assessed the quality of recommendations and examined several aspects of the recommendation process by using three datasets collected from two different courses.While the presented recommender system was built for the domain of introductory programming, our course-adaptive recommendation approach could be used in a variety of other domains. more »« less
Banjade, R.
(, he International FLAIRS Conference Proceedings, 34.)
null
(Ed.)
We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains.
Olson, Sabrina; Jia-Richards, Oliver; Johnson, Aaron W
(, American Society for Engineering Education)
Ethics and social responsibility education within aerospace engineering remains limited, with education on the subject often disconnected from technical course content and led by guest lecturers. While still valuable, this approach inadvertently signals to students that such topics are an addendum to their work as engineers, and reinforces the misconception of engineering as an apolitical field. Furthermore, existing ethical discussions place focus on the microethical realm, examining the ethical implications of individual decisions within the profession. This microethical focus, while important, overlooks the wider impact of engineering technologies on society. Contrastingly, macroethics addresses the collective social responsibility of the engineering field, emphasizing the ethical concerns of engineering technology. However, the abstract and qualitative nature of these macroethical concepts often conflicts with the more quantitative content of technical engineering classes, complicating efforts to integrate them into engineering coursework. This work-in-progress paper presents an example of how macroethical concepts can be embedded into traditional technical classes to foster student awareness of their ethical responsibilities as future engineers. An in-class macroethics activity and follow-up assignment were implemented in an aerospace engineering capstone design course at the University of Michigan. In the in-class activity, the technical concept of spaceports, or facilities designed for spacecraft launch, and the macroethical concepts of rightsholder analysis were specifically selected to complement the course topic of spacecraft systems design. As such, the course structure was designed to present macroethical considerations as equivalent to other systems design requirements. The in-class activity encompassed a full course period and was both developed and presented by the course instructor, with the follow-up assignment appearing in the final student group reports. The aim of the in-class activity was to increase student awareness of macroethical effects, asking the broader question of who/what is impacted when an engineering decision is made. To this end, activities of rightsholder identification and power-impact mapping were implemented, along with small-group and full-class dialogue. Students were asked to select a location for a spaceport within their university’s host state, consider the impact of their choice by identifying the rightsholders affected, and compare and contrast the differences in power and impact of these affected parties. Following the lesson, students repeated this process as part of their final course project, considering the social impacts as part of their space system design process. The instructor's experience of developing and implementing the in-class macroethics lesson and activities is examined within this paper, with focus placed on the decisions made within course structuring and lesson planning to present macroethical content as equivalent in importance to technical content. Discussion of learning goals and pedagogy will be shared with aims to identify key aspects of the macroethics lesson that may be implemented in other courses. Future work by the authors will seek to further develop this core set of facilitation goals, and integrate student data into evaluating effectiveness of the lesson in developing students’ macroethical awareness.
Core, Mark; Nye, Benjamin; Carr, Kayla; Li, Shirley; Shiel, Aaron; Auerbach, Daniel; Leeds, Andrew; Swartout, William
(, The International FLAIRS Conference Proceedings)
As AI tools become common across jobs and industries, it is critical to broaden education about AI beyond teaching computer scientists how to build AI systems. To expand AI education, we are researching AI for AI learning: a personalized and adaptive learning system that integrates dialog-based tutoring and gamified programming activities. To study this problem, we adapted and expanded an existing smartphone adaptive coach to develop the Game-if-AI system. Using a design-based research approach, Game-if-AI was iteratively tested and improved across four semesters of optional use in a course designed for technician-level understanding of AI: mastering programming skills to apply AI libraries and established models. In this study, we measured the interests and needs of these technical learners, based on both survey data and on how they engaged with topics in the system. Based on this data, new topics were added and the system was refined. In this paper, we report students' usability ratings for system components and student preferences based on completion rates of AI topics available each semester. Students rated the adaptive system positively overall (93% rated as a good idea), but more complex learning activities (tutoring dialogs, programming) were rated lower than traditional ones (e.g., multiple choice, reading). Students were most likely to master topics highly aligned to the course materials, as well as self-directed learning toward easier high-interest topics (e.g., LLM Prompting).
Barria-Pineda, Jordan; Sonmez_Unal, Deniz; Akhuseyinoglu, Kamil; Brusilovsky, Peter; Walker, Erin
(, Proceedings of 26th International Conference on Artificial Intelligence in Education, AIED 2025, Part 4, Springer Nature Switzerland)
Many educational recommender systems (EdRecSys) rely on commercial recommendation strategies that emphasize content relevance while neglecting learners’ views on recommendation effectiveness. To address this, we conducted a co-design study with computer science students in an introductory programming course to explore their vision of an ideal EdRecSys. The subjects shared preferences and concerns related to three areas: recommendation approaches, transparency, and control. We used Zimmerman’s model of self-regulated learning to contextualize their expectations within a broader educational framework. Findings offer actionable insights for designing learner-centered AIED systems that foster engagement, agency, and self-regulation.
Barria-Pineda, Jordan; Akhuseyinoglu, Kamil; Brusilovsky, Peter; Pollari-Malmi, Kerttu; Sirkiä, Teemu; Malmi, Lauri
(, Proceedings of Workshop on Adaptation and Personalization in Computer Science Education at the 28th ACM Conference on User Modeling, Adaptation and Personalization)
Personalized recommendation of learning content is one of the most frequently cited benefits of personalized online learning. It is expected that with personalized content recommendation students will be able to build their own unique and optimal learning paths and to achieve course goals in the most optimal way. However, in many practical cases students search for learning content not to expand their knowledge, but to address problems encountered in the learning process, such as failures to solve a problem. In these cases, students could be better assisted by remedial recommendations focused on content that could help in resolving current problems. This paper presents a transparent and explainable interface for remedial recommendations in an online programming practice system. The interface was implemented to support SQL programming practice and evaluated in the context of a large database course. The paper summarizes the insights obtained from the study and discusses future work on remedial recommendations.
Chau, Hung, Barria-Pineda, Jordan, and Brusilovsky, Peter.
"Course-Adaptive Content Recommender for Course Authoring". 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3–5, 2018 (). Country unknown/Code not available. https://doi.org/10.1007/978-3-319-93846-2_9.https://par.nsf.gov/biblio/10112140.
@article{osti_10112140,
place = {Country unknown/Code not available},
title = {Course-Adaptive Content Recommender for Course Authoring},
url = {https://par.nsf.gov/biblio/10112140},
DOI = {10.1007/978-3-319-93846-2_9},
abstractNote = {Developing online courses is a complex and time-consuming process that involves organizing a course into a sequence of topics and allocating the appropriate learning content within each topic. This task is especially difficult in complex domains like programming, due to the incremental nature of programming knowledge, where new topics extensively build upon domain concepts that were introduced in earlier lessons. In this paper, we propose a course-adaptive content-based recommender system that assists course authors and instructors in selecting the most relevant learning material for each course topic. The recommender system adapts to the deep prerequisite structure of the course as envisioned by a specific instructor, while unobtrusively deducing that structure from problem-solving examples that the instructor uses to present course concepts. We assessed the quality of recommendations and examined several aspects of the recommendation process by using three datasets collected from two different courses.While the presented recommender system was built for the domain of introductory programming, our course-adaptive recommendation approach could be used in a variety of other domains.},
journal = {13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3–5, 2018},
author = {Chau, Hung and Barria-Pineda, Jordan and Brusilovsky, Peter},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.
An official website of the United States government
