Self-Directed Learning/Self-Regulated Learning on Computer Simulated Environment

Abstract

Computer-based learning environments (CBLEs), such as game-based learning and simulation-based learning, provide learners the learning platform to self-manage their learning, including setting their own goals, selecting learning strategies, and attributing time to tasks or elements of learning tasks based on their judgment and needs, which will encourage leaners' agency of active involvement in the learning, boost their learning motivation and engagement, and require more self-regulated learning (SRL) skills. This study investigates the impact of self-managed learning within computer-simulated environments designed for complex scientific discovery. Using Zimmerman's cyclical self-regulated learning framework and goal-setting theory, the study explores how autonomy in learning—specifically through self-directed goal setting, time management, and strategy use—affects learning outcomes, judgment accuracy, and SRL behaviors. Experiment 1 reveals that learners who were free to self-pace their learning and determine the sequence of their tasks achieved significantly higher test scores than those in time- and order-yoked conditions. Experiment 2 mainly explores how goals could best support learners in self-regulated learning in the computer-simulated environment. The results indicate that when learners have specific goals, no matter if the goals were set by the learners or given to them, they demonstrate better learning outcomes. The findings underscore the importance of autonomy in enhancing SRL and indicate that clear, specific goals, whether self-determined or assigned, lead to better learning outcomes than general goals. Additionally, my study has identified five distinct SRL learner profiles. These profiles identified learners as "EXPERT SRL LEARNERS," who employ comprehensive strategies and achieve high test scores; "EFFICIENT STRATEGY-DRIVEN LEARNERS," who effectively use time to maximize their learning and have a high reliance on using prediction and summarization strategies; "BALANCED BUT TIME-INTENSIVE LEARNERS," who has a medium level of the test score, however, spend a longer time studying; "INCONSISTENT SRL LEARNERS," whose learning outcome is below average and frequently using strategies such as prior knowledge and prediction; and "Ineffective SRL Learners," who has a low test score and not often using any of these learning strategies. I have also found that learners have distinct motivational beliefs among these different profiles, such as their self-efficacy and goal orientation beliefs. The results suggested that promoting and training learners in self-efficacy, mastery goal setting, and effective SRL strategies are critical for academic success, especially in complex, computer-based learning environments. My research will contribute to understanding how self-regulatory processes and motivational factors interact to influence learning in technology-enhanced education and offers practical implications for designing interventions that support diverse learner needs.