Identifying the effects of scaffolding on learners' temporal deployment of self-regulated learning operations during game-based learning using multimodal data.

Introduction: Self-regulated learning (SRL), or learners' ability to monitor and change their own cognitive, affective, metacognitive, and motivational processes, encompasses several operations that should be deployed during learning including Searching, Monitoring, Assembling, Rehearsing, and Translating (SMART). Scaffolds are needed within GBLEs to both increase learning outcomes and promote the accurate and efficient use of SRL SMART operations. This study aims to examine how restricted agency (i.e., control over one's actions) can be used to scaffold learners' SMART operations as they learn about microbiology with Crystal Island, a game-based learning environment. Methods: Undergraduate students (N = 94) were randomly assigned to one of two conditions: (1) Full Agency, where participants were able to make their own decisions about which actions they could take; and (2) Partial Agency, where participants were required to follow a pre-defined path that dictated the order in which buildings were visited, restricting one's control. As participants played Crystal Island, participants' multimodal data (i.e., log files, eye tracking) were collected to identify instances where participants deployed SMART operations. Results: Results from this study support restricted agency as a successful scaffold of both learning outcomes and SRL SMART operations, where learners who were scaffolded demonstrated more efficient and accurate use of SMART operations. Discussion: This study provides implications for future scaffolds to better support SRL SMART operations during learning and discussions for future directions for future studies scaffolding SRL during game-based learning.

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis.

Undergraduate students (N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency, where they had complete control over their actions, and partial agency, where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.

Task Engagement and the Vigilance Decrement Revisited: Expanding Upon the Work of Joel S. Warm Using a Semantic Vigilance Paradigm.

OBJECTIVE: The goal of the present study is twofold: (1) demonstrate the importance of measuring and understanding the relationship between task engagement and vigilance performance, and (2) celebrate the work of Joel S. Warm and expand upon his previous research in two semantic vigilance paradigms. BACKGROUND: The importance of measuring task engagement in cognitive and sensory vigilance tasks has been well documented. But to date, our understanding of the effects of task engagement on semantic vigilance performance is limited. METHOD: Seventy-three participants completed either a standard semantic vigilance task or a lure semantic vigilance task. Participants also completed subjective measures of workload and stress. RESULTS: The results indicated that changes in task engagement are associated with correct detection performance. Changes in task engagement may be related to individual differences in the distress associated with performing semantic vigilance tasks. CONCLUSION: In line with the work of Warm and his colleagues (Dember, Warm, Bowers, & Lanzetta, 1984), participants who reported increased task engagement after the vigil outperformed their peers who noted decreased task engagement upon conclusion of the task. Participants reporting increases in engagement with the semantic vigilance tasks also reported significantly greater distress pretask, but not posttask. Instead, increases in postvigil distress were driven by the task to which participants were assigned, not task engagement. APPLICATION: The present study has several implications for applied settings that involve long duration semantic processing or semantic target identification. Such real-world tasks include aviation, cyber threat detection and analysis, driving, and reading.

The Effects of Event Rate on a Cognitive Vigilance Task.

OBJECTIVE: The present experiment sought to examine the effects of event rate on a cognitive vigilance task. BACKGROUND: Vigilance, or the ability to sustain attention, is an integral component of human factors research. Vigilance task difficulty has previously been manipulated through increasing event rate. However, most research in this paradigm has utilized a sensory-based task, whereas little work has focused on these effects in relation to a cognitive-based task. METHOD: In sum, 84 participants completed a cognitive vigilance task that contained either 24 events per minute (low event rate condition) or 40 events per minute (high event rate condition). Performance was measured through the proportion of hits, false alarms, mean response time, and signal detection analyses (i.e., sensitivity and response bias). Additionally, measures of perceived workload and stress were collected. RESULTS: The results indicated that event rate significantly affected performance, such that participants who completed the low event rate task achieved significantly better performance in terms of correction detections and false alarms. Furthermore, the cognitive vigil utilized in the present study produced performance decrements comparable to traditional sensory vigilance tasks. CONCLUSION: Event rate affects cognitive vigilance tasks in a similar manner as traditional sensory vigilance tasks, such that a direct relation between performance and level of event rate was established. APPLICATION: Cognitive researchers wishing to manipulate task difficulty in their experiments may use event rate presentation as one avenue to achieve this result.