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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 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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2. On Moving a Face-to-Face Flipped Classroom to a Remote Setting

   Autar Kaw (May 2021, 2021 ASEE Virtual Annual Conference)

   Starting in March 2020, the COVID19 pandemic instantly affected the education of 14 million higher education students in the USA. The switch to remote instruction caught instructors and students off guard – teachers had to change their techniques, approaches, and course content rapidly (called “panicgogy”), and students had to adjust to remote instruction in a hurry. Hoping that the pandemic would not last too long, most had expected to return to the regular class format at most by the Fall semester. That expectation was quickly squashed as the summer semester progressed. If one were teaching a face-to-face classroom in a flipped modality, it would be even more challenging to teach a flipped class in an online environment. In this paper, we present how the instructor overhauled a face-to-face flipped class in Numerical Methods to an online environment. This involved 1) rethinking the learning design of the course content via the learning management system, 2) using Microsoft forms as personal response systems, and YouTube for video lectures, 3) not only using break-out rooms for peer-to-peer learning but the “main room” for individual learning as well, 4) exploit the availability of two computers and multiple monitors to deliver and observe the synchronous part of the class, 5) use of discussion boards to streamline the flow of communication that would have otherwise been unwieldy for the instructor, TAs, and students alike, 6) changes made to assessment as it had to be carried online and within a proctoring software environment, 7) changes in the conducting of office hours. The above items will be discussed in the paper, and comparisons of face-to-face and online implementations will be made. The ultimate goal is to present a logic model for a typical lecture-based online flipped STEM classroom for efficient and effective implementation by other instructors. 

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3. The Effectiveness of Synchronous vs Asynchronous Modes of Instruction in an Online Flipped Design Thinking Course

   Mohandas, L. (July 2021, 2021 ASEE Annual Conference & Exposition)

   Motivation: This is a complete paper. There was a sudden shift from traditional learning to online learning in Spring 2020 with the outbreak of COVID-19. Although online learning is not a new topic of discussion, universities, faculty, and students were not prepared for this sudden change in learning. According to a recent article in ‘The Chronicle of Higher Education, “even under the best of circumstances, virtual learning requires a different, carefully crafted approach to engagement”. The Design Thinking course under study is a required freshmen level course offered in a Mid-western University. The Design Thinking course is offered in a flipped format where all the content to be learned is given to students beforehand and the in-class session is used for active discussions and hands-on learning related to the content provided at the small group level. The final learning objective of the course is a group project where student groups are expected to come up with functional prototypes to solve a real-world problem following the Design Thinking process. There were eighteen sections of the Design Thinking course offered in Spring 2020, and with the outbreak of COVID-19, a few instructors decided to offer synchronous online classes (where instructors were present online during class time and provided orientation and guidance just like a normal class) and a few others decided to offer asynchronous online classes (where orientation from the instructor was delivered asynchronous and the instructor was online during officially scheduled class time but interactions were more like office hours). Students were required to be present synchronously at the team level during the class time in a synchronous online class. In an asynchronous online class, students could be synchronous at the team level to complete their assignment any time prior to the deadline such that they could work during class time but they were not required to work at that time. Through this complete paper, we are trying to understand student learning, social presence and learner satisfaction with respect to different modes of instruction in a freshmen level Design Thinking course. Background: According to literature, synchronous online learning has advantages such as interaction, a classroom environment, and better course quality whereas asynchronous online learning has advantages such as self-controlled and self-directed learning. The disadvantages of synchronous online learning include the learning process, technology issues, and distraction. Social isolation, lack of interaction, and technology issue are a few disadvantages related to asynchronous online learning. Problem Being Addressed: There is a limited literature base investigating different modes of online instruction in a Design Thinking course. Through this paper, we are trying to understand and share the effectiveness of synchronous and asynchronous modes of instruction in an online Flipped Design Thinking Course. The results of the paper could also help in this time of pandemic by shedding light on the more effective way to teach highly active group-based classrooms for better student learning, social presence, and learner satisfaction. Method/Assessment: An end of semester survey was monitored in Spring 2020 to understand student experiences in synchronous and asynchronous Design Thinking course sections. The survey was sent to 720 students enrolled in the course in Spring 2020 and 324 students responded to the survey. Learning was measured using the survey instrument developed by Walker (2003) and the social presence and learner satisfaction was measured by the survey modified by Richardson and Swan (2003). Likert scale was used to measure survey responses. Anticipated Results: Data would be analyzed and the paper would be completed by draft paper submission. As the course under study is a flipped and active course with a significant component of group work, the anticipated results after analysis could be that one mode of instruction has higher student learning, social presence, and learner satisfaction compared to the other. 

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4. On learning platform metrics as markers for student success in a course

   https://doi.org/10.1002/cae.22653  

   Yalcin, Ali; Kaw, Autar; Clark, Renee (June 2023, Computer Applications in Engineering Education)

   Abstract Adaptive learning platforms are increasingly being used as part of varying instructional modalities. Particularly relevant to this paper, adaptive learning is a critical component of personalized, preclass learning in a flipped classroom. Previously inaccessible, data generated by adaptive learning platforms regarding student engagement with the course content provides an invaluable opportunity to gain a deeper understanding of the learning process and improve upon it. We aim to investigate the relationships between adaptive learning platform interactions and overall student success in the course and identify the variables most influential to student success. We present a comprehensive analysis of our adaptive learning platform data collected in a Numerical Methods course, including aggregate statistics, frequency analysis, and Principal Component Analysis, to determine which variables exhibited the most variability and, therefore, the most information in the data. Subsequently, we used the Partitioning Around Medoids clustering approach to investigate naturally occurring clusters of students and how these clusters relate to overall performance in the course. Our results show that overall performance in the course, as measured by the final course grade, is strongly associated with (1) the behavioral interactions of students with the adaptive platform and (2) their performance on the adaptive learning assessments. We also found distinct student clusters (as defined by success in the course) that exhibited distinctly different behaviors. These findings provide qualitative and quantitative information to identify students needing support and to craft an evidence‐based support strategy for these students. 

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5. Towards an Adaptive Learning Module for Materials Science: Comparing Expert Predictions to Student Performance

   Nutnicha Nigon; Dana C Simionescu; Thomas W Ekstedt; Julie D Tucker; Milo D Koretsky (June 2022, ASEE Annual Conference proceedings)

   The emphasis on conceptual learning and the development of adaptive instructional design are both emerging areas in science and engineering education. Instructors are writing their own conceptual questions to promote active learning during class and utilizing pools of these questions in assessments. For adaptive assessment strategies, these questions need to be rated based on difficulty level (DL). Historically DL has been determined from the performance of a suitable number of students. The research study reported here investigates whether instructors can save time by predicting DL of newly made conceptual questions without the need for student data. In this paper, we report on the development of one component in an adaptive learning module for materials science – specifically on the topic of crystallography. The summative assessment element consists of five DL scales and 15 conceptual questions This adaptive assessment directs students based on their previous performances and the DL of the questions. Our five expert participants are faculty members who have taught the introductory Materials Science course multiple times. They provided predictions for how many students would answer each question correctly during a two-step process. First, predictions were made individually without an answer key. Second, experts had the opportunity to revise their predictions after being provided an answer key in a group discussion. We compared expert predictions with actual student performance using results from over 400 students spanning multiple courses and terms. We found no clear correlation between expert predictions of the DL and the measured DL from students. Some evidence shows that discussion during the second step made expert predictions closer to student performance. We suggest that, in determining the DL for conceptual questions, using predictions of the DL by experts who have taught the course is not a valid route. The findings in this paper can be applied to assessments in both in-person, hybrid, and online settings and is applicable to subject matter beyond materials science. 

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