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1. One minute is enough: Early Prediction of Student Success and Event-level Difficulty during Novice Programming Tasks

   Mao, Y. ( January 2019 , In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019))

   Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be e effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this work, Recent Temporal Patterns (RTPs) are used in conjunction with Support Vector Machine and Logistic Regression to build robust yet interpretable models for early predictions. We performed two tasks: to predict student success and difficulty during one, open-ended novice programming task of drawing a square-shaped spiral. We comparedmore »RTP against several machine learning models ranging from the classic to the more recent deep learning models such as Long Short Term Memory to predict whether students would be able to complete the programming task. Our results show that RTP-based models outperformed all others, and could successfully classify students after just one minute of a 20- minute exercise (students can spend more than 1 hour on it). To determine when a system might intervene to prevent incompleteness or eventual dropout, we applied RTP at regular intervals to predict whether a student would make progress within the next fi ve minutes, reflecting that they may be having difficulty. RTP successfully classifi ed these students needing interventions over 85% of the time, with increased accuracy using data-driven program features. These results contribute signi ficantly to the potential to build a fully data-driven tutoring system for novice programming.« less
2. Introducing An RME-Based Task Sequence to Support the Guided Reinvention of Vector Spaces

   Plaxco, D. ( January 2021 , Proceedings of the Annual Conference on Research in Undergraduate Mathematics Education)

   S. S. Karunakaran, & A. (Ed.)

   In this paper, we introduce an RME-based (Freudenthal, 1991) task sequence intended to support the guided reinvention of the linear algebra topic of vector spaces. We also share the results of a paired teaching experiment (Steffe & Thompson, 2000) with two students. The results show how students can leverage their work in the problem context to develop more general notions of Null Space. This work informs further revisions to the task statements for using these materials in a whole-class setting.
3. Introducing An RME-Based Task Sequence to Support the Guided Reinvention of Vector Spaces

   Plaxco, D. ; Andrews-Larson, C. ; Smith, J. ; Kim, M. ; Mauntel, M. ; Watford, M. ( January 2021 , 2021 Research in Undergraduate Mathematics Education Reports)

   Karunakaran, S. ; Higgins, A. (Ed.)

   In this paper, we introduce an RME-based (Freudenthal, 1991) task sequence intended to support the guided reinvention of the linear algebra topic of vector spaces. We also share the results of a paired teaching experiment (Steffe & Thompson, 2000) with two students. The results show how students can leverage their work in the problem context to develop more general notions of Null Space. This work informs further revisions to the task statements for using these materials in a whole-class setting.
4. The Making of an Innovative Engineer: Academic and Life Experiences that Shape Engineering Task and Innovation Self-Efficacy.

   Schar, M. ; Gilmartin, S. ; Rieken, B. ; Brunhaver, S. ; Chen, H. ; Sheppard, S. ( January 2017 , Proceedings of the American Society for Engineering Education Annual Conference, June 25-28. Columbus, OH.)

   This research paper presents the results of a study that uses multivariate models to explore the relationships between participation in learning experiences, innovation self-efficacy, and engineering task self-efficacy. Findings show that many engineering students participated in learning experiences that are typically associated with engineering education, such as taking a shop class or engineering class in high school (47%), taking a computer science (81%) or design/prototyping (72%) class as an undergraduate, working in an engineering environment as an intern (56%), or attending a career related event during college (75%). Somewhat surprisingly, given the rigors of an engineering curriculum, a significant numbermore »of students participated in an art, dance, music, theater, or creative writing class (55%), taken a class on leadership topics (47%), and/or participated in student clubs outside of engineering (44%) during college. There also were important differences in rates of participation by gender, underrepresented racial/ethnic minority status, and first generation college student status. Overall prediction of engineering task self-efficacy and innovation self-efficacy was relatively low, with a model fit of these learning experiences predicting engineering task self-efficacy at (adjusted r2 of) .200 and .163 for innovation self-efficacy. Certain patterns emerged when the learning experiences were sorted by Bandura’s Sources of Self-Efficacy. For engineering task self-efficacy, higher participation in engineering mastery and vicarious engineering experiences was associated with higher engineering task self-efficacy ratings. For the development of innovation self-efficacy, a broader range of experiences beyond engineering experiences was important. There was a strong foundation of engineering mastery experiences in the innovation self-efficacy model; however, broadening experiences beyond engineering, particularly in the area of leadership experiences, may be a factor in innovation selfefficacy. These results provide a foundation for future longitudinal work probing specific types of learning experiences that shape engineering students’ innovation goals. They also set the stage for comparative models of students’ goals around highly technical engineering work, which allows us to understand more deeply how “innovation” and “engineering” come together in the engineering student experience.« less
5. Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

   https://doi.org/10.3390/electronics11091476  

   Poudyal, Sujan ; Mohammadi-Aragh, Mahnas J. ; Ball, John E. ( May 2022 , Electronics)

   The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can understand students’ attention. In a classroom where students have their own computers in front of them, it is important for instructors to understand whether students are paying attention. We collected on- and off-task data to analyze the attention behaviors of students. Educational data mining extracts hidden information from educational records, and we are using it to classifymore »student attention patterns. A hybrid method is used to combine various techniques like classifications, regressions, or feature extraction. In our work, we combined two feature extraction techniques: principal component analysis and linear discriminant analysis. Extracted features are used by a linear and kernel support vector machine (SVM) to classify attention patterns. Classification results are compared with linear and kernel SVM. Our hybrid method achieved the best results in terms of accuracy, precision, recall, F1, and kappa. Also, we correlated attention with learning. Here, learning corresponds to tests and a final course grade. For determining the correlation between grades and attention, Pearson’s correlation coefficient and p-value were used.« less