Title: Task-agnostic team competence assessment and metacognitive feedback for transparent project-based learning in data science

Authors: Hong Liu; Timothy Bernard; Elif Cankaya; Alex Hall

Addresses: Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA ' Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA ' Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA ' Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA

Abstract: Assessing team and individual competencies from team projects' outcomes alone can be pretty subjective. Sharing credit for team efforts equally between team members or differentiating individual contributions based on peer evaluations that might be prone to bias destroys motivation and hinders learning. A fair assessment of individual performances should depend on a formative assessment of a team's process and each individual's contribution to tasks. Such an assessment is time-consuming and only affordable to utilise in small classes. This research serves as a small step to synergise the human and Artificial Intelligence (AI) based educational technology to improve the transparency and effectiveness of collaborative Project-Based Learning (PBL). We introduce a web-bot (BotCaptain) to automate parts of the instructional tasks, present a task-agnostic team competency model, and recommends a set of metacognitive feedback for team members. Study findings have implications for the use of AI in PBL environments.

Keywords: teamwork; project based learning; computer supported collaborative learning; competency based learning assessment; metacognitive feedback; artificial intelligence.

DOI: 10.1504/IJSMARTTL.2023.129623

International Journal of Smart Technology and Learning, 2023 Vol.3 No.2, pp.138 - 162

Received: 25 Apr 2021
Received in revised form: 31 Dec 2021
Accepted: 08 Apr 2022
Published online: 17 Mar 2023 *

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