Since the 2014 high-profile meta-analysis of undergraduate STEM courses, active learning has become a standard in higher education pedagogy. One way to provide active learning is through the flipped classroom. However, finding suitable pre-class learning activities to improve student preparation and the subsequent classroom environment, including student engagement, can present a challenge in the flipped modality. To address this challenge, adaptive learning lessons were developed for pre-class learning for a course in Numerical Methods. The lessons would then be used as part of a study to determine their cognitive and affective impacts. Before the study could be started, it involved constructing well-thought-out adaptive lessons. This paper discusses developing, refining, and revising the adaptive learning platform (ALP) lessons for pre-class learning in a Numerical Methods flipped course.
In a prior pilot study at a large public southeastern university, the first author had developed ALP lessons for the pre-class learning for four (Nonlinear Equations, Matrix Algebra, Regression, Integration) of the eight topics covered in a Numerical Methods course. In the current follow-on study, the first author and two other instructors who teach Numerical Methods, one from a large southwestern urban university and another from an HBCU, collaborated on developing the adaptive lessons for the whole course.
The work began in Fall 2020 by enumerating the various chapters and breaking each one into individual lessons. Each lesson would include five sections (introduction, learning objectives, video lectures, textbook content, assessment). The three instructors met semi-monthly to discuss the content that would form each lesson. The main discussion of the meetings centered on what a student would be expected to learn before coming to class, choosing appropriate content, agreeing on prerequisites, and choosing and making new assessment questions. Lessons were then created by the first author and his student team using a commercially available platform called RealizeIT. The content was tested by learning assistants and instructors. It is important to note that significant, if not all, parts of the content, such as videos and textbook material, were available through previously done work. The new adaptive lessons and the revised existing ones were completed in December 2020.
The adaptive lessons were tested for implementation in Spring 2021 at the first author's university and made 15% of the students' grade calculation. Questions asked by students during office hours, on the LMS discussion board, and via emails while doing the lessons were used to update content, clarify questions, and revise hints offered by the platform. For example, all videos in the ALP lessons were updated to HD quality based on student feedback. In addition, comments from the end-of-semester surveys conducted by an independent assessment analyst were collated to revise the adaptive lessons further. Examples include changing the textbook content format from an embedded PDF file to HTML to improve quality and meet web accessibility standards.
The paper walks the reader through the content of a typical lesson. It also shows the type of data collected by the adaptive learning platform via three examples of student interactions with a single lesson.
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Student Engagement Dataset
A major challenge for online learning is the inability of systems to support student emotion and to maintain student engagement. In response to this challenge, computer vision has become an embedded feature in some instructional applications. In this paper, we propose a video dataset of college students solving math problems on the educational platform MathSpring.org with a front facing camera collecting visual feedback of student gestures. The video dataset is annotated to indicate whether students’ attention at specific frames is engaged or wandering. In addition, we train baselines for a computer vision module that determines the extent of student engagement during remote learning. Baselines include state-of-the-art deep learning image classifiers and traditional conditional and logistic regression for head pose estimation. We then incorporate a gaze baseline into the MathSpring learning platform, and we are evaluating its performance with the currently implemented approach.
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- Award ID(s):
- 1551572
- NSF-PAR ID:
- 10346041
- Date Published:
- Journal Name:
- 2021 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
- Page Range / eLocation ID:
- 3621 - 3629
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Since the 2014 high-profile meta-analysis of undergraduate STEM courses, active learning has become a standard in higher education pedagogy. One way to provide active learning is through the flipped classroom. However, finding suitable pre-class learning activities to improve student preparation and the subsequent classroom environment, including student engagement, can present a challenge in the flipped modality. To address this challenge, adaptive learning lessons were developed for pre-class learning for a course in Numerical Methods. The lessons would then be used as part of a study to determine their cognitive and affective impacts. Before the study could be started, it involved constructing well-thought-out adaptive lessons. This paper discusses developing, refining, and revising the adaptive learning platform (ALP) lessons for pre-class learning in a Numerical Methods flipped course. In a prior pilot study at a large public southeastern university, the first author had developed ALP lessons for the pre-class learning for four (Nonlinear Equations, Matrix Algebra, Regression, Integration) of the eight topics covered in a Numerical Methods course. In the current follow-on study, the first author and two other instructors who teach Numerical Methods, one from a large southwestern urban university and another from an HBCU, collaborated on developing the adaptive lessons for the whole course. The work began in Fall 2020 by enumerating the various chapters and breaking each one into individual lessons. Each lesson would include five sections (introduction, learning objectives, video lectures, textbook content, assessment). The three instructors met semi-monthly to discuss the content that would form each lesson. The main discussion of the meetings centered on what a student would be expected to learn before coming to class, choosing appropriate content, agreeing on prerequisites, and choosing and making new assessment questions. Lessons were then created by the first author and his student team using a commercially available platform called RealizeIT. The content was tested by learning assistants and instructors. It is important to note that significant, if not all, parts of the content, such as videos and textbook material, were available through previously done work. The new adaptive lessons and the revised existing ones were completed in December 2020. The adaptive lessons were tested for implementation in Spring 2021 at the first author's university and made 15% of the students' grade calculation. Questions asked by students during office hours, on the LMS discussion board, and via emails while doing the lessons were used to update content, clarify questions, and revise hints offered by the platform. For example, all videos in the ALP lessons were updated to HD quality based on student feedback. In addition, comments from the end-of-semester surveys conducted by an independent assessment analyst were collated to revise the adaptive lessons further. Examples include changing the textbook content format from an embedded PDF file to HTML to improve quality and meet web accessibility standards. The paper walks the reader through the content of a typical lesson. It also shows the type of data collected by the adaptive learning platform via three examples of student interactions with a single lesson.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCVW54120.2021.00405
