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Instructor-led presentation-based teaching mainly focuses on delivering content. Whereas student active presentations-based teaching approaches require students to take leadership in learning actions. Many teaching and learning strategies were adopted to foster active student participation during in-class learning activities. We developed the student presentation-based effective teaching (SPET) approach in 2014 to make student presentation activity the central element of learning challenging concepts. We have developed several versions to meet the need for teaching small classes (P. Tyagi, "Student Presentation Based Effective Teaching (SPET) Approach for Advanced Courses," in ASME IMECE 2016-66029, V005T06A026), large enrolment classes (P. Tyagi, "Student Presentation Based Teaching (SPET) Approach for Classes With Higher Enrolment," ASME IMECE 2018-88463, V005T07A035), and online teaching during COVID-19. (P. Tyagi, "Second Modified Student Presentation Based Effective Teaching (SPET) Method Tested in COVID-19 Affected Senior Level Mechanical Engineering Course," in ASME IMECE 2020-23615, V009T09A026). The SPET approach has successfully engaged students with varied interests and competence levels in the learning process. SPET approach has also made it possible to cover new topics such as training engineering students about positive intelligence skills to foster lifelong learning aptitude and doing engineering projects in a group setting. However, it was noted that many students who were overwhelmed with parallel academic demands in other courses and different activities were underperforming via SPET-based learning strategies. SPET core functioning depends on the following steps: Step 1: Provide a set of conceptual and topical questions for students to answer individually after self-education from the recommended textbook or course material, Step-2: Group presentations are prepared by the prepared students for in-class discussion, Step-3: Group makes a presentation in class 1-2 weeks after the day of the assignment to seek instructor feedback and to do peer discussion. The instructor noted that students unfamiliar with the new concepts and terminologies in the SPET assignment struggled to respond to questions individually and contribute to the group discussion based on their presentation. Several motivated students who invested time in familiarizing new concepts and terminologies met or exceeded the expectations. However, a significant student population struggled. To alleviate this issue author has implemented a further improvement in SPET approach. This paper reports teaching experiments conducted in MECH 487 Photovoltaic Cells and Solar Thermal Energy System and MECH 462 Design of Energy Systems course. This improvement requires augmenting SPET with instructor-led concept familiarization discussion on the day of issuing the assignment or close to that; for this step instructor utilized exemplary student work from prior SPET-based teaching of the same course. In the survey, many students expressed their views about the improvement and reported introductory discussions were helpful and addressed several reservations and impediments students encountered. This paper will discuss the structure of the new improvement strategy and outcomes-including student feedback and comments.
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This content will become publicly available on August 18, 2025
How Does a Data-Informed Deliberate Change in Learning Design Impact Students’ Self-Regulated Learning Tactics?
The current study measures the extent to which students’ self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access the learning material. The improved design gave students the choice to skip the first attempt and access the learning material directly. Student learning tactics were measured using a multi-level clustering and process mining algorithm, and a quasi-experiment design was implemented to remove or reduce differences in extraneous factors, including content being covered, time of implementation, and naturally occurring fluctuations in student learning tactics. The analysis suggests that most students who chose to skip the first attempt were effectively self-regulating their learning and were thus successful in learning from the instructional materials. Students who would have failed the first attempt were much more likely to skip it than those who would have passed the first attempt. The new design also resulted in a small improvement in learning outcome and median learning time. The study demonstrates the creation of a closed loop between learning design and learning analytics: first, using learning analytics to inform improvements to the learning design, then assessing the effectiveness and impact of the improvements.
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- Award ID(s):
- 1845436
- PAR ID:
- 10579994
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Journal of Learning Analytics
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 1929-7750
- Page Range / eLocation ID:
- 174 to 196
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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