More Like this

1. Development, Implementation, Refining and Revising of Adaptive Platform Lessons for an Engineering Course

   Kaw, A. ; Yalcin, A. ; Gomes, R. ; Serrano, L. ; Scott, A. ; Lou, Y. ; Clark, R. ( July 2022 , Proceedings of ASEE Annual Conference and Exposition)

   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 lessonsmore »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.« less
2. Development, Implementation, Refining and Revising of Adaptive Platform Lessons for an Engineering Course

   Kaw, A. ( June 2023 , Proceedings of the 2022 ASEE Annual Conference and Exposition)

   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 lessonsmore »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.« less
3. Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education

   https://doi.org/10.1109/FIE.2018.8659291  

   Deng, Yuli ; Lu, Duo ; Chung, Chun-Jen ; Huang, Dijiang ; Zeng, Zhen ( October 2018 , IEEE Frontiers in Education Conference (FIE))

   This Innovate Practice full paper presents a cloud-based personalized learning lab platform. Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. With that in mind, ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoThmore »Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.« less
4. Implications of Gamification in Learning Environments on Computer Sci-ence Students: A Comprehensive Study

   Zahedi, L. Ross ( June 2019 , 126th Annual Conference and Exposition of American Society for Engineering Education)

   Computers are used in almost all the fields in our daily life –they are used in various occupations and do the tasks with greater precision and as a result, made the life more comfortable. As such, more than 500,000 computing jobs remain unfulfilled in the US (Reported by app association), and many nations need more computer scientist. Therefore, this urge the need for engineering education community and researchers to focus more on underrepresentation of women in CS due to the fact that women currently comprise only 15.7% of computing degrees awarded ; Computer Science has one of the most considerable gender disparities in science, technology, and engineering and the number of female students choosing computer science as their major remains underrepresented regardless of recent improvements; and the reason behind this statistic is the challenges that lessen students’ motivation in CS majors; Programming courses have always had a negative image among students and usually need more practice. In order to increase the number of female students in CS and ensure the health of the community, there is a need to better understand and discover a mechanism that can improve women’s participation in computer science which leads to attracting more female studentsmore »in computer science. Researchers have explored various engagements strategies in the fields of computer science. One of the strategies that have seen an increase and garnered attention in the last two decades is the use of video game elements or gamification in different fields such as education. Gamification -which usually refers to using video game mechanics in activities not related to video games - aims to increase participants’ engagement and enjoyment. This notion has been increasing popularity over time especially among especially education researchers because game elements -which provide challenges to the players and motivate them to set goals- can be used in learning environments appropriately to enhance the motivation of learners. While there is a strong body of literature around the implications of gamification on student learning, there are inconsistent results in the literature with regards to the interests or attitudes of women. This review aims to provide a critical evaluation of the use of gamification in the application in the existing literature in 1) education 2) computer science and 3) women in computer science to provide a basis for more targeted learning engagement strategies to motivate and retain more women in computing fields and build on the literature on gamification and gender.« less
5. Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System

   https://doi.org/10.1109/FG52635.2021.9667001  

   Ruiz, Nataniel ; Yu, Hao ; Allessio, Danielle A. ; Jalal, Mona ; Joshi, Ajjen ; Murray, Thomas ; Magee, John J. ; Whitehill, Jacob R. ; Ablavsky, Vitaly ; Arroyo, Ivon ; et al ( December 2021 , 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021))

   In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants ofmore »a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.« less