Search

Online instructional design and how to engage students cognitively in online asynchronous courses have been an ongoing question. This case study presents an intentional design of an asynchronous online graduate course to foster cognitive presence. The research questions investigate students' cognitive presence (CP) captured by two measures: Community of Inquiry (CoI) survey (for self-report) and Linguistic Inquiry and Word Count (LIWC) software (for actual behaviors) in this online course. Additionally, it also addresses how cognitive presence is related to other presences and how the online course design elements were perceived by students. Results showed that students perceived high levels of cognitive presence and they showed high cognitive presence in their discussion board acts. There was a relationship between three presences; and findings showed that teacher and social presence were strong predictors of perceived cognitive presence. Although students in the study rated themselves high on the CoI instrument and scored high on the LIWC for cognitive presence, self-presentation bias still emerged. Strategies that helped students to stay cognitively present in this asynchronous online course included: instructor responsiveness in discussion posts and creating dialogue, creating course assignments as online hands-on project, interviewing guest speakers on specific course topics, weekly recap and orientation videos, feedback, case-based discussions, and other elements.

Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on and the practice of reflection is often limited to pre-service training or short professional development cycles. This study explores how natural language processing, deep-learning methods can be used to support continuous teacher reflection by facilitating data collection and analysis in any instructional setting that includes ample linguistic and assessment material. Data was collected from an existing introductory undergraduate biology course. A Bidirectional Long-Short Term Memory network was trained to predict assessment item difficulty and tasked with assigning difficulty to recorded lectures. Comparison with the instructor's perceptions of lecture material difficulty suggested the model was highly reliable at predicting difficult lecture material. We discuss how this model could be expanded into an AI toolkit meant to aid in teacher reflection on their practices and curriculum.

This exploratory paper highlights how problem-based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from "Crystal 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.