Search for: All records

For nearly two decades, conversational agents have been used to structure group interactions in online chat-based environments. More recently, this form of dynamic support for collaborative learning has been extended to physical spaces using a combination of multimodal sensing technologies and instrumentation installed within a physical space. This demo extends the reach of dynamic support for collaboration still further through an application of what has recently been termed on-device machine learning, which enables a portable form of multimodal detection to trigger real-time responses. 

Computer science pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this paper, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob Programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computer science education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future. 

Contributing to the literature on aptitude-treatment interactions between worked examples and problem-solving, this paper addresses differential learning from the two approaches when students are positioned as domain experts learning new concepts. Our evaluation is situated in a team project that is part of an advanced software engineering course. In this course, students who possess foundational domain knowledge but are learning new concepts engage alternatively in programming followed by worked example-based reflection. They are either allowed to finish programming or are curtailed after a pre-specified time to participate in a longer worked example-based reflection. We find significant pre- to post-test learning gains in both conditions. Then, we not only find significantly more learning when students participated in longer worked example-based reflections but also a significant performance improvement on a problem-solving transfer task. These findings suggest that domain experts learning new concepts benefit more from worked example-based reflections than from problem-solving. 

For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios. 

Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no-support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent’s primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used. 

Purpose In response to the evolving COVID-19 pandemic, many universities have transitioned to online instruction. With learning promising to be online, at least in part, for the near future, instructors may be thinking of providing online collaborative learning opportunities to their students who are increasingly isolated from their peers because of social distancing guidelines. This paper aims to provide design recommendations for online collaborative project-based learning exercises based on this research in a software engineering course at the university level. Design/methodology/approach Through joint work between learning scientists, course instructors and software engineering practitioners, instructional design best practices of alignment between the context of the learners, the learning objectives, the task and the assessment are actualized in the design of collaborative programming projects for supporting learning. The design, first segments a short real-time collaborative exercise into tasks, each with a problem-solving phase where students participate in collaborative programming, and a reflection phase for reflecting on what they learned in the task. Within these phases, a role-assignment paradigm scaffolds collaboration by assigning groups of four students to four complementary roles that rotate after each task. Findings By aligning each task with granular learning objectives, significant pre- to post-test learning from the exercise as well as each task is observed. Originality/value The roles used in the paradigm discourage divide-and-conquer tendencies often associated with collaborative projects. By requiring students to discuss conflicting ideas to arrive at a consensus implementation, their ideas are made explicit, thus providing opportunities for clarifying misconceptions through discussion and learning from the collaboration.