Journal of Computing in Higher Education最新文献

This study investigated the effects of pre-training types on cognitive load, self-efficacy, and problem-solving in computer programming. Pre-training was provided to help learners acquire schemas related to problem-solving strategies. 84 undergraduate students were randomly assigned to one of three groups and each group received three different types of pre-training: 1) WOE (worked-out example) and metacognitive scaffolding, 2) faded WOE and metacognitive scaffolding, and 3) WOE and faded metacognitive scaffolding. After the pre-training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving skills were analyzed. Then, during the training phase, the participants were asked to attempt a programming problem-solving task with faded WOE and faded metacognitive scaffoldings. After the training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving were analyzed again. The findings revealed that providing both cognitive scaffolding (i.e., WOE or faded WOE) and non-faded metacognitive scaffolding during the pre-training phase is effective for novice learners for optimizing cognitive load, promoting self-efficacy, and enhancing programming problem-solving skills.

The purpose of this multiple-case study was to investigate how expert instructors in case-based learning (CBL) conceptualize, structure, facilitate, and assess asynchronous online discussions while addressing challenges. Accordingly, we first interviewed three expert instructors with extensive teaching experience using CBL in higher education in online learning environments and then observed their online courses. Results indicated that (a) how instructors conceptualize case discussions may relate to different conceptualizations of their instructor role, (b) instructors typically clustered facilitation strategies creating posts that included content expertise, social congruence, and cognitive congruence, but how these strategies were implemented differed across instructors, and (c) instructors differentiated solutions to challenges associated with observed difficulties on the instructor and student side. Implications for practice and research are provided.

Laboratory activities are central to undergraduate student learning in science and engineering. With advancements in computer technology, many laboratory activities have shifted from providing students experiments in a physical mode to providing them in a virtual mode. Further, physical and virtual modes can be combined to address a single topic, as the modes have complementary affordances. In this paper, we report on the design and implementation of a physical and virtual laboratory on the topic of jar testing, a common process for drinking water treatment. The assignment for each laboratory mode was designed to leverage the mode’s affordances. Correspondingly, we hypothesized each would elicit a different subset of engineering epistemic practices. In a naturalistic, qualitative study design based on laboratory mode (physical or virtual) and laboratory order (virtual first or physical first), we collected process, product, and reflection data of students’ laboratory activity. Taking an orientation that learning is participation in valued disciplinary practice, data were coded and used to characterize how students engaged with each laboratory mode. Results showed that the virtual laboratory elicited more conceptual epistemic practices and the physical laboratory more material epistemic practices, aligning with the affordances of each mode. When students completed the laboratory in the virtual mode first, students demonstrated greater engagement in epistemic practices and more positive perceptions of their learning experience in the virtual mode than when they completed the physical mode first. In contrast, engagement in the physical mode was mostly unaffected by the laboratory order.

Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students’ reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed virtual patients in an intelligent tutoring system. The think-aloud data were transcribed and segmented into 2,792 meaningful units. We used a text mining tool to analyze the linguistic features of think-aloud segments. Meanwhile, we manually coded the think-aloud segments using a medical reasoning coding scheme. We then trained eight types of supervised machine learning algorithms to predict reasoning activities based on the linguistic features of students’ think-aloud transcripts. We further investigated if the performance of prediction models differed between high and low performers. The results suggested that students’ reasoning activities could be predicted relatively accurately by the linguistic features of their think-aloud transcripts. Moreover, training the predictive models using the data instances of either high or low performers did not lower the models’ performance. This study has significant methodological and practical implications regarding the automatic analysis of think-aloud protocols and real-time assessment of students’ reasoning activities.

Research in computer-supported collaborative learning has explored various ways to support learner-learner interaction as healthcare professionals engage in online formats. While studies have explored various socio-emotional learning outcomes, learners’ psychological safety has yet to be explored as healthcare professionals engage in collaborative problem-solving. To address this gap, the qualitative study employed semi-structured interviews to understand occupational therapy students’ (N = 10) perceptions of psychological safety as they engaged in an online learning class. The resulting themes of this study described the feelings associated with different forms of interactions requiring psychological safety: (a) being vulnerable, (b) fear of being misunderstood, (c) need to protect/protection, and (d) group cohesion. The findings have implications for online learner-learner interactions and computer-supported collaborative learning. For example, learners discussed how the perceived permanence of online learning lead to a sense of self-preservation and reticence to discuss the ill-structured and potentially controversial nature of complex problems. Additional aspects of psychological safety in online learning highlighted the importance of shared experiences, learning from failure, and community building.

Learning management systems (LMS) have emerged as a standard component of higher education institutions for the web-based delivery and management of courses. The COVID-19 pandemic highlighted the value of LMS in facilitating online teaching and learning. However, the significance of examining the factors that impact LMS use success during the pandemic has been underestimated. Moreover, despite previous attempts to evaluate students’ LMS experience, most research failed to connect the actual use of LMS to students’ learning success. To address these gaps, we developed and validated an empirical and theory-based instrument measuring students’ LMS experience. The choice of constructs was informed by a scoping review of LMS measures and interviews with a representative sample of students and teachers about their LMS use. By adding constructs that are relevant to learning in the LMS, the current study provided a more comprehensive measurement that captures students’ learning experience in the platform. We provided evidence for the measurement invariance of the scales with their Chinese translation as well. By addressing the limitations and building on this study’s findings, researchers can further advance our understanding of LMS experiences and contribute to developing more effective e-learning systems to support teaching and learning in higher education.

Information problem solving (IPS) is an important twenty-first century skill, but it is lacking at all age levels. One type of information problem, those of an ill-structured nature that require multiple iterations of (re)defining problems and formulating emerging solutions, can be particularly challenging but have received less attention in the IPS literature. Further, the process of solving such problems often reveals, while simultaneously being impacted by, problem solvers’ epistemic beliefs. Using a self-regulated problem-solving model as an analytic framework and taking advantage of multiple data sources, this study examined college students’ self-regulatory patterns in performing an ill-structured IPS task, and compared the patterns displayed by two groups of students with more and less adaptive epistemic beliefs. Sequential analysis of behavioral data revealed different patterns between the two groups. Think-aloud data, interviews, and students’ IPS products showed three key differences between the two groups: difference in the roles of IPS task instructions, difference in the numbers and triggers of queries, and qualitative difference in iterations between page viewing and writing. The findings yielded important insights into the self-regulatory processes of IPS and the role of epistemic beliefs at different problem-solving stages. Implications are drawn for educators and learning designers for developing IPS in higher education.

In the flipped classroom, students engage in preparatory activities to study the course materials prior to attending teacher-guided sessions. Students’ success in the flipped classroom is directly related to their preparation and students tend to change their preparation activity over time. Few studies have investigated why students change their preparation activity. Therefore, we address this gap by first clustering university students (N = 174) enrolled in a flipped course for the first time based on their preparatory activities at three time points. We identified distinct preparatory activity patterns by computing changes in cluster membership. Next, we compared students’ preparatory activity patterns in course performance, motivation, and self-regulation. The temporal investigation of activity patterns provided important insights into how preparation (or lack thereof) at different phases relates to course performance. Intensive preparation only at the beginning of the course was related to significantly worse course performance whereas preparation only in the middle of the course was related to higher course performance. Students who performed intensively during the course had significantly higher course performance, higher intrinsic motivation at the beginning, and higher self-regulation (in particular, time management) in the middle of the course than students showing lower activity during preparation. Our findings provide important implications for future research and educational practice, particularly for students transitioning to flipped classroom learning for the first time.

Abstract

Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in Knowledge Building inquiries with facilitation through analytics-supported reflective assessment, while the comparison class (n = 38) pursued Knowledge Building inquiries facilitated by portfolio-supported reflective assessment. This study found that analytics-supported reflective assessment positively and significantly influenced undergraduates’ collective knowledge advancement. Path analysis revealed the mechanisms of analytics-supported reflective assessment for supporting undergraduates’ collective knowledge advancement—the undergraduates’ metacognitive engagement and cognitive engagement influenced each other, further influencing their contribution to collective knowledge advancement and domain understanding. This study holds significant practical implications for fostering students’ knowledge building, inquiry, and metacognition by designing technology-enhanced learning environments as collaborative and metacognitive tools. Additionally, the study offers insights into the processes and mechanisms of reflective assessment, contributing to an understanding of how it enhances students’ development of higher-order skills.

Abstract

This profiling study deals with the self-regulated learning skills of online learners based on their interaction behaviors on the learning management system. The learners were profiled through their interaction behaviors via cluster analysis. Following a correlational model with the interaction data of learners, the post-test questionnaire data were used to determine self-regulated learning skills scores during the learning process. Regarding the scores, the clusters were named through the prominent interactions of the learners yielding three clusters; actively engaged (Cluster1), assessment-oriented (Cluster2), and passively-oriented (Cluster3), respectively. The profiles in the clusters indicate that assessments were mostly used by the learners in Cluster2, while the frequency of the content tools was high in Cluster1. Surprisingly, some tools such as glossary, survey, and chat did not play a prominent role in discriminating the clusters. Suggestions for future implementations of self-regulated learning and effective online learning in learning management systems are also included.