- Award ID(s):
- 2114789
- NSF-PAR ID:
- 10401617
- Date Published:
- Journal Name:
- Annual Meeting of the American Educational Research Association 2023.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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There is growing evidence of the effectiveness of project-based learning (PBL) in preparing students to solve complex problems. In PBL implementations in engineering, students are treated as professional engineers facing projects centered around real-world problems, including the complexity and uncertainty that influence such problems. Not only does this help students to analyze and solve an authentic real-world task, promoting critical thinking, but also students learn from each other, learning valuable communication and teamwork skills. Faculty play an important part by assuming non-conventional roles (e.g., client, senior professional engineer, consultant) to help students throughout this instructional and learning approach. Typically in PBLs, students work on projects over extended periods of time that culminate in realistic products or presentations. In order to be successful, students need to learn how to frame a problem, identify stakeholders and their requirements, design and select concepts, test them, and so on. Two different implementations of PBL projects in a fluid mechanics course are presented in this paper. This required, junior-level course has been taught since 2014 by the same instructor. The first PBL project presented is a complete design of pumped pipeline systems for a hypothetical plant. In the second project, engineering students partnered with pre-service teachers to design and teach an elementary school lesson on fluid mechanics concepts. With the PBL implementations, it is expected that students: 1) engage in a deeper learning process where concepts can be reemphasized, and students can realize applicability; 2) develop and practice teamwork skills; 3) learn and practice how to communicate effectively to peers and to those from other fields; and 4) increase their confidence working on open-ended situations and problems. The goal of this paper is to present the experiences of the authors with both PBL implementations. It explains how the projects were scaffolded through the entire semester, including how the sequence of course content was modified, how team dynamics were monitored, the faculty roles, and the end products and presentations. Students' experiences are also presented. To evaluate and compare students’ learning and satisfaction with the team experience between the two PBL implementations, a shortened version of the NCEES FE exam and the Comprehensive Assessment of Team Member Effectiveness (CATME) survey were utilized. Students completed the FE exam during the first week and then again during the last week of the semester in order to assess students’ growth in fluid mechanics knowledge. The CATME survey was completed mid-semester to help faculty identify and address problems within team dynamics, and at the end of the semester to evaluate individual students’ teamwork performance. The results showed that no major differences were observed in terms of the learned fluid mechanics content, however, the data showed interesting preliminary observations regarding teamwork satisfaction. Through reflective assignments (e.g., short answer reflections, focus groups), student perceptions of the PBL implementations are discussed in the paper. Finally, some of the challenges and lessons learned from implementing both projects multiple times, as well as access to some of the PBL course materials and assignments will be provided.more » « less
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Abstract This exploratory paper highlights how problem‐based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from C
rystal Island: EcoJourneys , a collaborative game‐based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys , students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in‐game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game‐based learning environment? Drawing on a mixed method approach, we first analyzed students' pre‐ and post‐test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre‐ and post‐test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game‐based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game‐based learning environment: students engaged in low to moderate self‐directed actions with (1) high and (2) moderate collaborative sense‐making actions, (3) low self‐directed with low collaborative sense‐making actions and (4) high self‐directed actions with low collaborative sense‐making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self‐directed and high collaborative sense‐making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in‐game activities.Practitioner notes What is already known about this topic
Learning analytic methods have been effective for understanding student learning interactions for the purposes of assessment, profiling student behaviour and the effectiveness of interventions.
However, the interpretation of analytics from these diverse data sets are not always grounded in theory and challenges of interpreting student data are further compounded in collaborative inquiry settings, where students work in groups to solve a problem.
What this paper adds
Problem‐based learning as a pedagogical framework allowed for the design to focus on individual and collaborative actions in a game‐based learning environment and, in turn, informed the interpretation of game‐based analytics as it relates to student's self‐directed learning in their individual investigations and collaborative inquiry discussions.
The combination of principal component analysis and qualitative interaction analysis was critical in understanding the nuances of student collaborative inquiry.
Implications for practice and/or policy
Self‐directed actions in individual investigations are critical steps to collaborative inquiry. However, students may need to be encouraged to engage in these actions.
Clustering student data can inform which scaffolds can be delivered to support both self‐directed learning and collaborative inquiry interactions.
All students can engage in knowledge‐integration discourse, but some students may need more direct support from teachers to achieve this.
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