TY - GEN
T1 - Do instrumentation tools capture self-regulated learning?
AU - Van Der Graaf, Joep
AU - Lim, Lyn
AU - Fan, Yizhou
AU - Kilgour, Jonathan
AU - Moore, Johanna
AU - Bannert, Maria
AU - Gasevic, Dragan
AU - Molenaar, Inge
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/4/12
Y1 - 2021/4/12
N2 - Researchers have been struggling with the measurement of Self-Regulated Learning (SRL) for decades. Instrumentation tools have been proposed to help capture SRL processes that are difficult to capture. The aim of the present study was to improve measurement of SRL by embedding instrumentation tools in a learning environment and validating the measurement of SRL with these instrumentation tools using think aloud. Synchronizing log data and concurrent think aloud data helped identify which SRL processes were captured by particular instrumentation tools. One tool was associated with a single SRL process: the timer co-occurred with monitoring. Other tools co-occurred with a number of SRL processes, i.e., the highlighter and note taker captured superficial writing down, organizing, and monitoring, whereas the search and planner tools revealed planning and monitoring. When specific learner actions with the tool were analyzed, a clearer picture emerged of the relation between the highlighter and note taker and SRL processes. By aligning log data with think aloud data, we showed that instrumentation tool use indeed reflects SRL processes. The main contribution is that this paper is the first to show that SRL processes that are difficult to measure by trace data can indeed be captured by instrumentation tools such as high cognition and metacognition. Future challenges are to collect and process log data real time with learning analytic techniques to measure ongoing SRL processes and support learners during learning with personalized SRL scaffolds.
AB - Researchers have been struggling with the measurement of Self-Regulated Learning (SRL) for decades. Instrumentation tools have been proposed to help capture SRL processes that are difficult to capture. The aim of the present study was to improve measurement of SRL by embedding instrumentation tools in a learning environment and validating the measurement of SRL with these instrumentation tools using think aloud. Synchronizing log data and concurrent think aloud data helped identify which SRL processes were captured by particular instrumentation tools. One tool was associated with a single SRL process: the timer co-occurred with monitoring. Other tools co-occurred with a number of SRL processes, i.e., the highlighter and note taker captured superficial writing down, organizing, and monitoring, whereas the search and planner tools revealed planning and monitoring. When specific learner actions with the tool were analyzed, a clearer picture emerged of the relation between the highlighter and note taker and SRL processes. By aligning log data with think aloud data, we showed that instrumentation tool use indeed reflects SRL processes. The main contribution is that this paper is the first to show that SRL processes that are difficult to measure by trace data can indeed be captured by instrumentation tools such as high cognition and metacognition. Future challenges are to collect and process log data real time with learning analytic techniques to measure ongoing SRL processes and support learners during learning with personalized SRL scaffolds.
KW - Instrumentation tools
KW - Self-regulated learning
UR - http://www.scopus.com/inward/record.url?scp=85103877456&partnerID=8YFLogxK
U2 - 10.1145/3448139.3448181
DO - 10.1145/3448139.3448181
M3 - Conference contribution
AN - SCOPUS:85103877456
T3 - ACM International Conference Proceeding Series
SP - 438
EP - 448
BT - LAK 2021 Conference Proceedings - The Impact we Make
PB - Association for Computing Machinery
T2 - 11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021
Y2 - 12 April 2021 through 16 April 2021
ER -