Engagement in Academic Work: The Role of Learning Goals, Future Consequences, Pleasing Others, and Perceived Ability   总被引：2，自引：0，他引：2  

Raymond B. Miller  Barbara A. Greene  Gregory P. Montalvo  Bhuvaneswari Ravindran  Joe D. Nichols 《Contemporary educational psychology》1996,21(4):388-422

Engagement in academic work was viewed from a multiple goals perspective. Two studies were conducted in which high school math students completed an instrument measuring five goals students might have for doing academic work (learning goals, performance goals, obtaining future consequences, pleasing the teacher, and pleasing the family), perceived math ability, self-regulatory activities, strategies (deep or shallow) used when studying for math, and the amount of effort and persistence expended on the class. Factor analysis indicated that the five goals scales and the perceived ability scale represented unique factors. The correlations among the variables revealed theoretically consistent interrelationships. Multiple regression analyses indicated that various goals (e.g., learning goals, obtaining future consequences, and pleasing the teacher), perceived ability, and some interactions accounted for significant amounts of variance in the task engagement measures (self-regulation, strategy use, effort, and persistence) and achievement. Results are discussed in relation to current theory and their practical implications.  相似文献   

Teacher blogs and education policy in a publicly private world: filling the gap between policy and practice     

Kiersten Greene 《Learning, Media and Technology》2017,42(2):185-197

K-12 teachers who write about their daily work on blogs and other social media platforms share local knowledge and wisdom from the immediate view of the classroom. These first-hand retellings of everyday classroom life offer a unique critique of educational policy from the teachers’ perspective. Reporting on the analysis of 14 public-facing blogs written by New York City public school teachers, this paper highlights the possibilities held in these digital narratives by painting a picture of how teachers respond to the implementation of new educational policies.  相似文献   

Leveraging complexity frameworks to refine theories of engagement: Advancing self-regulated learning in the age of artificial intelligence     

Jonathan C. Hilpert  Jeffrey A. Greene  Matthew Bernacki 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1204-1221

Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

Practitioner notes

What is already known about this topic

• Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
• SRL is a dynamic, temporal process that leads to purposeful student engagement.
• Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.

What this paper adds

• A Markov process for measuring dynamic SRL processes using log data.
• Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
• Evidence that SRL processes can be meaningfully impacted through educational intervention.

Implications for theory and practice

• Complexity approaches inform theory and measurement of dynamic SRL processes.
• Static representations of dynamic SRL processes are promising learning analytics metrics.
• Engineered features of LMS usage are valuable contributions to AI models.

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