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.

Fatigue Incident Antecedents, Consequences, and Aviation Operational Risk Management Resources.

BACKGROUND: Flight crew fatigue is an important factor in aviation, leading organizations to implement fatigue risk management programs to reduce risk. The U.S. Air Force Air Mobility Command (AMC) has implemented the Aviation Operational Risk Management (AvORM) program to aid mission schedulers and flight crews in mitigating flight risks and identifying appropriate levels of risk. The AvORM program uses a scheduling tool and underpinning biomathematical fatigue model. This study examined self-reported fatigue-related incidents within AMC, which provides some indirect and anecdotal evidence as to the effectiveness of the scheduling tool. METHODS: Archival data from the AMC Aviation Safety Action Program (ASAP) Safety Reporting System was examined. Report content themes were created through an inductive approach in terms of fatigue prevalence, antecedents, and consequences. RESULTS: Fatigue was estimated as a factor in 4% of the reports. The two most commonly referenced fatigue antecedents were associated with mission/duty length and mission scheduling/planning factors. Factors associated with aircraft operation violations were the most cited consequences of fatigue. Fatigue was almost twice as likely to be reported as a secondary rather than primary contributing factor. Aircrew reported both positive and negative aspects of AvORM resources in mission planning and fatigue mitigation. DISCUSSION: Examination of ASAP reports suggests that fatigue is a contributing factor to safety incidents. Although the AvORM program highlights potential flight risks by utilizing a scheduling tool built upon an underlying biomathematical fatigue model, human fatigue continues to impact safety, suggesting an ongoing need for improved fatigue risk management and mitigation.Morris MB, Wiedbusch MD, Gunzelmann G. Fatigue incident antecedents, consequences, and aviation operational risk management resources. Aerosp Med Hum Perform. 2018; 89(8):708-716.