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1. Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems

   Banjade, R. ; Oli, P. ; Rus, V. ( May 2023 , Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891)

   Frasson, C. ; Mylonas, P. ; Troussas, C. (Ed.)

   Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others. 

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2. Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization

   https://doi.org/10.1145/3450613.3456831  

   Wang, Chunpai ; Sahebi, Shaghayegh ; Zhao, Siqian ; Brusilovsky, Peter ; Moraes, Laura O. ( June 2021 , Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization)

   null (Ed.)

   Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students’ historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, such as multiple-choice questions, they do not perform well for modeling complex problem solving in students. Most importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge. However, for complex problems that involve many concepts at the same time, this assumption is deficient. It results in inaccurate knowledge states and unnecessary fluctuations in estimated student knowledge, especially if students guess the correct answer to a problem that they have not mastered all of its concepts or slip in answering the problem that they have already mastered all of its concepts. In this paper, we argue that not all attempts are equivalently important in discovering students’ knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students’ performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students’ discovered knowledge states and helps in discovering complex latent concepts in the problems. 

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3. Formative Feedback on Student-Authored Summaries in Intelligent Textbooks Using Large Language Models

   https://doi.org/10.1007/s40593-024-00395-0  

   Morris, Wesley ; Crossley, Scott ; Holmes, Langdon ; Ou, Chaohua ; Dascalu, Mihai ; McNamara, Danielle ( March 2024 , International Journal of Artificial Intelligence in Education)

   As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically provide feedback to students about the quality of summaries written at the end of intelligent textbook sections. The study builds on the work of Botarleanu et al. (2022), who used a Longformer Large Language Model (LLM) to develop a summary grading model. Their model explained around 55% of holistic summary score variance as assigned by human raters. This study uses a principal component analysis to distill summary scores from an analytic rubric into two principal components – content and wording. This study uses two encoder-only classification large language models finetuned from Longformer on the summaries and the source texts using these principal components explained 82% and 70% of the score variance for content and wording, respectively. On a dataset of summaries collected on the crowd-sourcing site Prolific, the content model was shown to be robust although the accuracy of the wording model was reduced compared to the training set. The developed models are freely available on HuggingFace and will allow formative feedback to users of intelligent textbooks to assess reading comprehension through summarization in real time. The models can also be used for other summarization applications in learning systems.

    

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4. Empowering computing students with proficiency in robotics via situated learning

   https://doi.org/10.1186/s40561-021-00167-6  

   Wang, Weitian ; Coutras, Constantine ; Zhu, Michelle ( October 2021 , Smart Learning Environments)

   With the increasing employment of robots in multiple areas such as smart manufacturing and intelligent transportation, both undergraduate and graduate students from computing related majors (e.g., computer science and information technology) demonstrated strong interests in learning robotics technology to broaden their career opportunities. However, instilling computing students with robotics knowledge remains a challenge since most of them have limited pre-training in engineering subjects such as electronics and mechatronics. Therefore, robotics education for computing students demands an immersive real-world learning environment by considering both theories and intensive hands-on projects. Different from traditional textbook-directed robotics learning, in this study, a situated learning-based robotics education pedagogy is proposed for computing students to equip them with robotics expertise and foster their problem-solving skills in real-world human–robot interaction contexts. To create a realistic human–robot collaboration situation, a multi-modal collaborative robot is employed in the classroom-based learning community for the whole semester. Mini-project-based homework and team projects are designed for students to practice their critical thinking and hands-on experiences. The bidirectional-evaluation approach is utilized by the instructor and students to assess the quality of the proposed pedagogy. Practice results and student evaluations suggested that the proposed situated learning-based pedagogy and robotics curriculum provided computing students to learn robotics in an effective way, which was well recognized and accepted by students even most of them were beginners. Future work of this study is also discussed.

    

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5. Using Engineering Design Tasks to Create Indigenous Cultural and Community Connections with the Classroom for Elementary and Middle School Students

   Bowman, F. ; Klemetsrud, B. ; Ozturk, E. ; Robinson, J. ( August 2022 , 2022 ASEE Annual Conference & Exposition)

   Oftentimes engineering design tasks are thought of as acultural and devoid of community inclusion and values. However, engineering design is inherently a cultural endeavor. Problems needing engineering solutions or design thinking are situated in a specific community and need community solutions. This work in progress paper describes initial efforts from a project to help elementary and middle school teachers create culturally relevant engineering design tasks for implementation in their classrooms. To integrate best practices for culturally relevant pedagogy, the engineering design framework developed by UTeach Engineering was adapted to specifically address community needs and cultural values. Changes to the framework also include culturally relevant instructional strategies for classroom implementation. To situate the engineering design steps within a culturally relevant framework questions involving communities and students’ cultural needs, values, and expectations were posed in each stage of the design process. A water filtration engineering design task was situated in the cultural concept of “Mni Wiconi” (Water is life in the Dakota language). This was taught in a summer professional development workshop for a cohort of elementary and middle school teachers, in rural North Dakota, with school districts comprised of large Native American student populations. Teachers adapted this design task for their individual classrooms and content areas (science, math, social studies, ELA) and implemented it in their classrooms in the fall of 2021. Additional support for teachers was provided with fall workshop days aimed at helping them with the facilitation of a culturally relevant engineering task. To integrate culturally relevant teaching and good engineering design tasks, the North Dakota Department of Public Instruction’s Native American Essential Understandings Teachings of our Elder’s website was used. This allowed teachers and students to have firsthand knowledge of how various science and engineering concepts are framed within the indigenous community. Professional development focused on how to situate culturally responsive teaching in engineering design. For example, in one of the school districts the water filtration task was related to increased pollution of a nearby lake which holds significant importance for the local Tribal Nation. In addition to being able to visibly witness the demand for cleaner water, the book “We are Water Protectors” written by Carole Lindstrom, was used to provide cultural grounding for the Identify and Describe stages of the engineering design framework. Case studies of how teachers incorporated the water filtration design task into their lesson plans are presented along with their suggestions on how to improve classroom implementation. Future work in the program includes teachers and their students developing engineering design tasks situated in their own communities and cultures. 

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