In problem-based learning (PBL), individual differences in students’ use of metacognition and self-regulation skills exist and calls for extensive research in postsecondary STEM education. This study focuses on students’ uncertainty management in PBL. A scale of the uncertainty management in PBL
(UM-PBL) was developed. Exploratory factor analysis was conducted and showed that the UM-PBL has substantial reliability and a total of 14 items across three constructs of a) perception of uncertainty
in learning to solve problems, b) self-efficacy in and c) strategy for uncertainty management. Gender differences in the first two constructs were found, confirming its known-group validation. Students’ problem-solving scores were positively correlated with scores of the first two constructs, suggesting
its predictability of its relationship with academic performance.
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Problems of Problem-Based Learning: Exploring Meta-Agency in Problem-Based Cybersecurity Learning in College Education
In college cybersecurity education, problem-based learning has been introduced to promote student agency in solving a complex problem. However, a dilemma of balancing the student agency persist and previous research has focused on students’ cognitive, metacognitive, and regulatory to enhance
the efficacy of PBL. Given the importance of students’ self-awareness of their agency, this study suggests a concept of meta-agency as an essential learner characteristic that influences the effectiveness of student agency in PBL. Four dimensions of meta-agency, perceptions of productive struggle, expectation alignment between instructor and students, strategies for regulating agency, and familiarity with PBL tasks, were qualitatively explored with student interview data. Features of meta-agency and how students’ meta-agency level develop through cybersecurity PBL sessions were further investigated.
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- Award ID(s):
- 2114789
- PAR ID:
- 10401618
- 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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Lecture-based teaching paired with laboratory-based exercises is most commonly used in cybersecurity instruction. However, it focuses more on theories and models but fails to provide learners with practical problem-solving skills and opportunities to explore real-world cybersecurity challenges. Problem-based Learning (PBL) has been identified as an efficient pedagogy for many disciplines, especially engineering education. It provides learners with real-world complex problem scenarios, which encourages learners to collaborate with classmates, ask questions and develop a deeper understanding of the concepts while solving real-world cybersecurity problems. This paper describes the application of the PBL methodology to enhance professional training-based cybersecurity education. The authors developed an online laboratory environment to apply PBL with Knowledge-Graph (KG) based guidance for hands-on labs in cybersecurity training.Learners are provided access to a virtual lab environment with knowledge graph guidance to simulated real-life cybersecurity scenarios. Thus, they are forced to think independently and apply their knowledge to create cyber-attacks and defend approaches to solve problems provided to them in each lab. Our experimental study shows that learners tend to gain more enhanced learning outcomes by leveraging PBL with knowledge graph guidance, become more aware of cybersecurity and relevant concepts, and also express interest in keep learning of cybersecurity using our system.more » « less
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Training doctoral students in critical thinking and experimental design using problem-based learningAbstract Background Traditionally, doctoral student education in the biomedical sciences relies on didactic coursework to build a foundation of scientific knowledge and an apprenticeship model of training in the laboratory of an established investigator. Recent recommendations for revision of graduate training include the utilization of graduate student competencies to assess progress and the introduction of novel curricula focused on development of skills, rather than accumulation of facts. Evidence demonstrates that active learning approaches are effective. Several facets of active learning are components of problem-based learning (PBL), which is a teaching modality where student learning is self-directed toward solving problems in a relevant context. These concepts were combined and incorporated in creating a new introductory graduate course designed to develop scientific skills (student competencies) in matriculating doctoral students using a PBL format. Methods Evaluation of course effectiveness was measured using the principals of the Kirkpatrick Four Level Model of Evaluation. At the end of each course offering, students completed evaluation surveys on the course and instructors to assess their perceptions of training effectiveness. Pre- and post-tests assessing students’ proficiency in experimental design were used to measure student learning. Results The analysis of the outcomes of the course suggests the training is effective in improving experimental design. The course was well received by the students as measured by student evaluations (Kirkpatrick Model Level 1). Improved scores on post-tests indicate that the students learned from the experience (Kirkpatrick Model Level 2). A template is provided for the implementation of similar courses at other institutions. Conclusions This problem-based learning course appears effective in training newly matriculated graduate students in the required skills for designing experiments to test specific hypotheses, enhancing student preparation prior to initiation of their dissertation research.more » « less
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null (Ed.)Abstract Problem-based learning (PBL) has been effectively used within BME education, though there are several challenges in its implementation within courses with larger enrollments. Furthermore, the sudden transition to online learning from the COVID-19 pandemic introduced additional challenges in creating a similar PBL experience in an online environment. Online constrained PBL was implemented through asynchronous modules and synchronous web conferencing with rotating facilitators. Overall, facilitators perceived web conferencing facilitation to be similar to in-person, but noted that students were more easily “hidden” or distracted. Students did not comment on web conferencing facilitation specifically, but indicated the transition to online PBL was smooth. Course instructors identified that a fully synchronous delivery as well as modifications of Group Meeting Minutes assignments as potential modifications for future offerings. Future work will aim to address the perceptions and effectiveness of web conferencing facilitation for PBL courses within an undergraduate BME curriculum, as web conferencing could prove to be another significant breakthrough in addressing challenges of problem-based learning courses.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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