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In this article, we explored the potential of the Student Mental Model Analyzer for Research and Teaching (SMART) technology that was designed to help students write and revise high-quality summaries of complex texts. Grounded in the design research, the aim of this pilot study was twofold: (a) to generate insights to further improve the SMART technology as a product and (b) to provide design knowledge that could guide the development and implementation of a formative assessment and feedback technology for reading comprehension. To that end, we conducted a formative evaluation of a beta version of the SMART technology in the context of a graduate-level asynchronous online course in which 21 students were enrolled. To investigate the potential of SMART and user experiences, we analyzed mixed data from the perspectives of the Technology Acceptance Model (i.e., ease of use and usefulness) and the quality aspects of learning technology (i.e., content, interface, and support). Results indicate that students improved their reading comprehension through SMART feedback and valued their use of SMART. The findings of the study also suggest design improvements for SMART. Furthermore, this research highlights the design approaches to formative assessment and feedback tools, focusing on improving users' technology acceptance.

Writing academic arguments is a complex and demanding task, even for proficient tertiary students. At the same time, providing prompt support for individual students working on discipline-specific arguments is often challenging for instructors. Artificial Intelligence (AI) techniques enable automated and adaptive educational scaffolding. In this study, we leveraged Natural Language Processing (NLP) AI techniques in an AI-Supported Scaffolding (AISS) system to evaluate written arguments and present alternative writing examples that human experts might write. To evaluate a pilot version of AISS, we gathered mixed-method data from 14 students enrolled in two sections of the same graduate-level online course (6 students in Cohort 1 and 8 students in Cohort 2). We used the Tool for the Automatic Analysis of Cohesion (TAACO) to track revisions in written arguments. TAACO indices demonstrated that the students used AI-generated scaffolding to build stronger claims with more elaborate and cohesive ideas. In written reflections, students revealed the perceived value of AISS, and visual inspection of their writing indicates that students used AISS feedback to improve their arguments. The findings demonstrate the potential of AI to provide personalised scaffolding for academic argument composition.

Summary writing is a useful instructional tool for learning. However, summary writing is a challenge to many students. This mixed-method study examined the potential of the Student Mental Model Analyzer for Research and Teaching (SMART) system to help students produce summaries that reflect key concepts and relations in a text. SMART uses the students' summary to generate a multi-dimensional 3S (surface, structure, semantic) evaluation of the students' mental model. This model is then used to drive feedback to help students revise their summary. The current study is an initial investigation examining whether writing and revising in SMART improves students' summary quality. Students (n = 38) in a graduate-level online course used SMART for seven reading assignments. The 38 students submitted a total of 357 summaries in response to the seven readings. In 47 cases, students produced both an initial draft and a modified revision. These 47 cases were selected for analysis. In the quantitative phase, MANOVA results indicated that students' summaries improved along the 3S dimensions from initial draft to revision. In the qualitative phase, inspection of exemplar cases revealed how students' mental models changed towards more robust and cohesive knowledge structure for texts.

During reading, students construct mental models of what they read. Summaries can be used to evaluate the latent knowledge structure of these mental models. We used indices from Student Mental Model Analyzer for Research and Teaching (SMART) to explore the potential of a global index, Graph Centrality (GC), as a measure to describe mental model structure and its relation to the quality of student summaries (e.g., the amount of content-coverage). Students (n = 73) in an online graduate-level course wrote and revised summaries of their course readings. Data preview left the total count of 32 cases to evaluate how students' mental representations changed from initial to final version. These summaries were analyzed using indices derived from the 3S model (surface, structure, semantic) as well as a measure of GC. The results of this initial investigation are promising, demonstrating that Graph Centrality captures important differences in students' summaries, including revision behaviors to the wholistic structure of mental models, modification trajectories toward a cohesive and solid mental representation that is semantically similar to the expert model.

Despite the importance of preparing students to write successfully in their academic and professional careers, instructors often struggle to sustain student focus on the complex and demanding nature of the writing process. In response, we conducted a pilot project at a university located in urban Los Angeles using a wiki-enhanced and blended writing course designed to sustain appropriate learner engagement. This exploratory project (a) introduced the hybrid writing course, (b) tracked changes in student engagement levels (i.e., behavioral, affective, and cognitive) over the period of the wiki-enhanced writing course, and (c) measured the impact of engagement on writing performance. Multiple data sources (i.e., wiki log data, student surveys, and writing performance scores) collected from 56 students in three sections of the writing course were used to examine student experiences in the wiki-enhanced writing course. The findings showed that wiki-based online discussion improved behavioral, affective, and cognitive engagement. The relationships between the learner engagement domains were reciprocal, temporal, and changeable. Based on the findings of this study and our review of the literature, we proposed a theoretical model to describe possible relationships between behavioral, affective, and cognitive engagement and academic achievement.

While key concepts embedded within an expert's textual explanation have been considered an aspect of expert model, the complexity of textual data makes determining key concepts demanding and time consuming. To address this issue, we developed Student Mental Model Analyzer for Teaching and Learning (SMART) technology that can analyze an experts' textual explanation to elicit an expert concept map from which key concepts are automatically derived. SMART draws on four graph-based metrics (i.e., clustering coefficient, betweenness, PageRank, and closeness) to automatically filter key concepts from experts' concept maps. This study investigated which filtering method extract key concepts most accurately. Using 18 expert textual data, we compared the accuracy levels of those four competing filtering methods by referring to four accuracy measures (i.e., precision, recall, F-measure, and N-similarity). The results showed the PageRank filtering method outperformed the other methods in all accuracy measures. For example, on average, PageRank derived 79% of key concepts as accurately as human experts. SMART's automatic filtering methods can help human experts save time when building an expert model, and it can validate their decision making on a list of key concepts.

In this study, we examined the role of leadership styles and multi-dimensional learner engagement in how students emerge as learning leaders in asynchronous online discussions. Grounded in the conceptual framework of two dominant leadership styles of transformational and transactional leadership, this study applies the two leadership styles--transformational leadership and transactional leadership--to the Leader Identification Method (LIM) which defines three types of leader roles (i.e., full, transactional and attractive facilitator) in online learning. We collected data from 20 students enrolled in a graduate-level online course that required participation in 12-week discussion activities. Results of the longitudinal data analyses show that person-focused, transformational leadership and active participation in online discussions were significant factors that enabled students to emerge as learning leaders. Students are more likely to become leaders by exhibiting transformational leadership behaviour and productively interacting with one another in an online discussion community.

Individual's risk perception regarding specific hazards is a dynamic process that evolves over time. This study analyzed the relationship between the number of COVID‐19 cases and the South Korean public's risk perceptions from the outset of the pandemic to the recent past. More than 70 repeated cross‐sectional surveys were conducted biweekly to measure individuals' risk perception. An autoregressive integrated moving average with explanatory variable time series analysis was used to characterize the relationship between the number of COVID‐19 cases and level of risk perceptions. It revealed that individuals' risk perception and the number of COVID‐19 cases were not linearly related but were logarithmically correlated. This finding can be understood as a psychic numbing effect, suggesting that people's perception of risk is not linear but rather exponentially sensitive to changes. The findings also revealed a significant influence of individuals' trust in local governments on their risk perceptions, highlighting the substantial role played by local governments in direct risk management during the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

We investigated relationships among expressed emotions, perceived motivation, perceived emotions, and three dependent variables (i.e., behavioral engagement, cognitive engagement, and student achievement) in the context of a blended online course. We defined "expressed emotions" as emotions detected by an affective computing tool in messages that students posted to online discussion forums in a blended undergraduate writing course. The results of two-step hierarchical multivariate regressions revealed that expressed emotions differentiated positive emotions from negative emotions better than perceived emotions did. Moreover, while no significant effect emerged for perceived motivation and perceived emotions, expressed fear was a significant predictor of student achievement (i.e., final score). Although affective computing is in its infancy, our findings suggest the potential use of expressed emotions for educational research and practice.