Copious research demonstrates the benefits of adding active learning to traditional lectures to enhance learning and reduce failure/withdrawal rates. However, many questions remain about how best to implement active learning to maximize student outcomes. This paper investigates several “second generation” questions regarding infusing active learning, via Think-Pair-Share (TPS), into a large lecture course in Computer Science. During the “Share” phase of TPS, what is the best way to debrief the associated course concepts with the entire class? Specifically, does student learning differ when instructors debrief the rationale for every answer choice (full debrief) versus only the correct answer (partial debrief)? And does the added value for student outcomes vary between tasks requiring recall versus deeper comprehension and/or application of concepts? Regardless of discipline, these questions are relevant to instructors implementing TPS with multiple-choice questions, especially in large lectures. Similar to prior research, when lectures included TPS, students performed significantly better (~13%) on corresponding exam items. However, students’ exam performance depended on both the type of debrief and exam questions. Students performed significantly better (~5%) in the full debrief condition than the partial debrief condition. Additionally, benefits of the full debrief condition were significantly stronger (~5%) for exam questions requiring deeper comprehension and/or application of underlying Computer Science processes, compared to simple recall. We discuss these results and lessons learned, providing recommendations for how best to implement TPS in large lecture courses in STEM and other disciplines.

Labs in a large lecture course provide highly scaffolded programming exercises. Despite positive student feedback, some students were not achieving the learning goals. This project attempted to increase student's conceptual understanding using short active learning exercises. The first iteration of this project used two versions of active learning: present several multiple-choice questions about the lab material, then either provide a full debrief of all answers or a partial debrief of only the correct answer. The control group was the previous semester's course when no debriefing was done. Two research questions were evaluated. First, does debriefing enhance learning in a computer science course? We found that students performed significantly better on debriefed items compared to students that were not debriefed. These results were unaffected by individual student GPA as a predictor. Second, does level of depth of debriefing affect learning? We found no significant difference between full debrief and partial debrief groups. These results were replicated in a second trial that compared full, partial, and no debriefing. In the second iteration of the project, each of three sections were given a full debrief, partial debrief, or no debrief (only the correct answer was given with no explanation). The full debrief group and the partial debrief group used the Think-Pair-Share method: pairs of students discussed the questions and various answers; one pair was chosen at random to answer each question. The research question is, does the level of depth of the debriefing intervention among the three treatments affect learning?

As the Internet of Things (IoT) continues its expansion into homes, businesses, government, and industries, the impact for computer science educators is amplified. In 2017, the ITiCSE IoT working group identified relevant content, tools for teaching, and four IoT course types. The resulting report provided an entry point for educators challenged with setting up a new IoT course, but did not consider the curricular content of a standalone specialization nor effective teaching approaches for this interdisciplinary field. In this report, the 2018 working group builds on its prior work through an updated review of literature and interviews with IoT instructors. The report addresses two research questions. First, what should a curriculum intended to produce IoT specialists include? We propose here a transdisciplinary curriculum that integrates threads from several disciplines on a single campus and we relate it to the ACM/IEEE 2013 Computing Curricula Knowledge Areas. Second, what pedagogical practices should be used to teach IoT? We found very little scholarship describing actual teaching practices, but our interviewees described their approaches and challenges in teaching. We present these as well as descriptions of several relevant teaching approaches in the report.

As the Internet of Things (IoT) continues its expansion into homes, businesses and industries, the impact for Computer Science educators is increasingly evident. In 2017, the ITiCSE IoT working group identified relevant content, tools for teaching, and four IoT course types. The resulting report provided an entry point for educators challenged with setting up a new IoT course. The 2018 working group will build on this prior work by addressing feedback on the 2017 report and by examining the rapidly changing state-of-the-art in IoT. In particular, the working group will extend educators' ability to integrate IoT into curriculum by investigating additional content areas and pedagogical approaches.