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1. Learning from Non-Assessed Resources: Deep Multi-Type Knowledge Tracing

   Wang, Chunpai ; Zhao, Siqian ; Sahebi, Shaghayegh ( January 2021 , Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021)

   The state of the art knowledge tracing approaches mostly model student knowledge using their performance in assessed learning resource types, such as quizzes, assignments, and exercises, and ignore the non-assessed learning resources. However, many student activities are non-assessed, such as watching video lectures, participating in a discussion forum, and reading a section of a textbook, all of which potentially contributing to the students' knowledge growth. In this paper, we propose the  first novel deep learning based knowledge tracing model (DMKT) that explicitly model student's knowledge transitions over both assessed and non-assessed learning activities. With DMKT we can discover the underlying latent concepts of each non-assessed and assessed learning material and better predict the student performance in future assessed learning resources. We compare our proposed method with various state of the art knowledge tracing methods on four real-world datasets and show its effectiveness in predicting student performance, representing student knowledge, and discovering the underlying domain model.
2. Undergraduate Student Learning of Market-Driven Design Topics in a Third-Year Design Course

   Hoffenson, Steven ; Pitterson, Nicole ( July 2021 , 2021 ASEE Virtual Annual Conference)

   Generally, the focus of undergraduate engineering programs is on the development of technical skills and how they can be applied to design and problem solving. However, research has shown that there is also a need to expose students to business and society factors that influence design in context. This technical bias is reinforced by the available tools for use in engineering education, which are highly focused on ensuring technical feasibility, and a corresponding lack of tools for engineers to explore other design needs. One important contextual area is market systems, where design decisions are made while considering factors such as consumer choice, competitor behavior, and pricing. This study examines student learning throughout a third-year design course that emphasizes market-driven design through project-based activities and assignments, including a custom-made, interactive market simulation tool. To bridge the gap between market-driven design and engineering education research, this paper explores how students think about and internally organize design concepts before and after learning and practicing market-driven design approaches and tools in the context of an engineering design course. The central research question is: In what ways do student conceptions of product design change after introducing a market-driven design curriculum? In line with the constructivismmore »framework of learning, it is expected that student conceptions of design should evolve to include more market considerations as they learn about and apply market-driven design concepts and techniques to their term projects. Four different types of data instruments are included in the analysis: Concept maps generated by the students before and after the course, open-ended written reflection assignments at various points in the semester, surveys administered after learning the market simulation tool and at the end of the course, and final project reports in which student teams listed their top 3-5 lessons learned in the course. Using the changes between the pre- and post-course concept maps as the primary metric to represent evolving design conceptions, data from the reflections, surveys, and reports are evaluated to assess their influence on such learning. Because market-driven design is a multi-faceted topic, a market-driven design is hierarchically decomposed into specific sub-topics for these evaluations. These include profitability (which itself encompasses pricing and costs), modeling and simulation, and market research (which encompasses consumers and competition). For each topic, correlation analyses are performed and regression models are fit to assess the significance of different factors on learning. The findings provide evidence regarding the effectiveness of the course’s market-driven design curriculum and activities on influencing student conceptions of design.« less
3. Undergraduate Student Learning of Market-Driven Design Topics in a Third-Year Design Course

   Hoffenson, Steven Pitterson ( July 2021 , American Society of Engineering Education)

   Generally, the focus of undergraduate engineering programs is on the development of technical skills and how they can be applied to design and problem solving. However, research has shown that there is also a need to expose students to business and society factors that influence design in context. This technical bias is reinforced by the available tools for use in engineering education, which are highly focused on ensuring technical feasibility, and a corresponding lack of tools for engineers to explore other design needs. One important contextual area is market systems, where design decisions are made while considering factors such as consumer choice, competitor behavior, and pricing. This study examines student learning throughout a third-year design course that emphasizes market-driven design through project-based activities and assignments, including a custom-made, interactive market simulation tool. To bridge the gap between market-driven design and engineering education research, this paper explores how students think about and internally organize design concepts before and after learning and practicing market-driven design approaches and tools in the context of an engineering design course. The central research question is: In what ways do student conceptions of product design change after introducing a market-driven design curriculum? In line with the constructivismmore »framework of learning, it is expected that student conceptions of design should evolve to include more market considerations as they learn about and apply market-driven design concepts and techniques to their term projects. Four different types of data instruments are included in the analysis: Concept maps generated by the students before and after the course, open-ended written reflection assignments at various points in the semester, surveys administered after learning the market simulation tool and at the end of the course, and final project reports in which student teams listed their top 3-5 lessons learned in the course. Using the changes between the pre- and post-course concept maps as the primary metric to represent evolving design conceptions, data from the reflections, surveys, and reports are evaluated to assess their influence on such learning. Because market-driven design is a multi-faceted topic, a market-driven design is hierarchically decomposed into specific sub-topics for these evaluations. These include profitability (which itself encompasses pricing and costs), modeling and simulation, and market research (which encompasses consumers and competition). For each topic, correlation analyses are performed and regression models are fit to assess the significance of different factors on learning. The findings provide evidence regarding the effectiveness of the course’s market-driven design curriculum and activities on influencing student conceptions of design.« less
4. Rank-Based Tensor Factorization for Student Performance Prediction

   Doan, T.N. ; Sahebi, S. ( January 2019 , 12th International Conference on Educational Data Mining (EDM))

   One of the essential problems, in educational data mining, is to predict students' performance on future learning materials, such as problems, assignments, and quizzes. Pioneer algorithms for predicting student performance mostly rely on two sources of information: students' past performance, and learning materials' domain knowledge model. The domain knowledge model, traditionally curated by domain experts maps learning materials to concepts, topics, or knowledge components that are presented in them. However, creating a domain model by manually labeling the learning material can be a difficult and time-consuming task. In this paper, we propose a tensor factorization model for student performance prediction that does not rely on a predefined domain model. Our proposed algorithm models student knowledge as a soft membership of latent concepts. It also represents the knowledge acquisition process with an added rank-based constraint in the tensor factorization objective function. Our experiments show that the proposed model outperforms state-of-the-art algorithms in predicting student performance in two real-world datasets, and is robust to hyper-parameters.
5. Mastery Learning in Undergraduate Engineering Courses: A Systematic Review

   Perez Leon, C. ; Verdin, D. ( August 2022 , Zone 1 Conference of the American Society for Engineering Education)

   This theory paper focuses on understanding how mastery learning has been implemented in undergraduate engineering courses through a systematic review. Academic environments that promote learning, mastery, and continuous improvement rather than inherent ability can promote performance and persistence. Scholarship has argued that students could achieve mastery of the course material when the time available to master concepts and the quality of instruction was made appropriate to each learner. Increasing time to demonstrate mastery involves a course structure that allows for repeated attempts on learning assessments (i.e., homework, quizzes, projects, exams). Students are not penalized for failed attempts but are rewarded for achieving eventual mastery. The mastery learning approach recognizes that mastery is not always achieved on first attempts and learning from mistakes and persisting is fundamental to how we learn. This singular concept has potentially the greatest impact on students’ mindset in terms of their belief they can be successful in learning the course material. A significant amount of attention has been given to mastery learning courses in secondary education and mastery learning has shown an exceptionally positive effect on student achievement. However, implementing mastery learning in an undergraduate course can be a cumbersome process as it requires instructors tomore »significantly restructure their assignments and exams, evaluation process, and grading practices. In light of these challenges, it is unclear the extent to which mastery learning has been implemented in undergraduate engineering courses or if similar positive effects can be found. Therefore, we conducted a systematic review to elucidate, how in the U.S., (1) has mastery learning been implemented in undergraduate engineering courses from 1990 to the present time and (2) the student outcomes that have been reported for these implementations. Using the systematic process outlined by Borrego et al. (2014), we surveyed seven databases and a total of 584 articles consisting of engineering and non-engineering courses were identified. We focused our review on studies that were centered on applying the mastery learning pedagogical method in undergraduate engineering courses. All peer-reviewed and practitioner articles and conference proceedings that were within our scope were included in the synthetization phase of the review. Most articles were excluded based on our inclusion and exclusion criteria. Twelve studies focused on applying mastery learning to undergraduate engineering courses. The mastery learning method was mainly applied on midterm exams, few studies used the method on homework assignments, and no study applied the method to the final exam. Students reported an increase in learning as a result of applying mastery learning. Several studies reported that students’ grades in a traditional final exam were not affected by mastery learning. Students’ self-reported evaluation of the course suggests that students prefer the mastery learning approach over traditional methods. Although a clear consensus on the effect of the mastery learning approach could not be achieved as each article applied different survey instruments to capture students’ perspectives. Responses to open-ended questions have mixed results. Two studies report more positive student comments on opened-ended questions, while one study report receiving more negative comments regarding the implementation of the mastery learning method. In the full paper we more thoroughly describe the ways in which mastery learning was implemented along with clear examples of common and divergent student outcomes across the twelve studies.« less