Uncovering the Effect of Prior Knowledge and Self-Efficacy on Self-Assessment and Task Selection in Learner-Controlled Instruction

Author(s):

Loredana Mihalca(presenting / submitting)Wolfgang Schnotz

Conference:

ECER 2012

Network:

11. Educational Effectiveness and Quality Assurance

Format:

Paper

Session Information

11 SES 10 A, Management and Learning Assessment for Educational Effectiveness

Parallel Paper Session

Time:

2012-09-20

15:30-17:00

Room:

FCT - Aula 1

Chair:

Lynne Grant

Contribution

Although in learner-controlled instruction, learners are given the opportunity to set their own learning trajectory (Corbalan, Kester, & Van Merriënboer, 2006), this leads to no improvement or even hampers performance according to empirical results (e.g., Mihalca, Salden, Corbalan, Paas, & Miclea 2011). A possible explanation for why learners, especially low prior knowledge students, do not benefit from learner control is that they are unable to accurately monitor, and assess their own performance as well as select an appropriate new learning task, in other words, do not possess the necessary self-directed learning (SDL) skills (Knowles, 1975). These skills are important prerequisites for implementing learner-controlled instruction successfully (Kostons, Van Gog, & Paas, 2010).

Accurate monitoring, self-assessment and task selection seem to be especially difficult for novices, presumably not only because of their lack of prior knowledge and additional cognitive demands, but also because of the lack of knowledge about performance criteria and standards (Van Gog & Paas, 2009). However, very little research has been conducted on exploring in-depth how students with different prior knowledge levels self-assess their performance, select new tasks and regulate their own learning in electronic learner-controlled environments. Therefore, the purpose of this study was to provide insight into the differences in self-assessment and task selection processes between low and high prior knowledge students engaged in an electronic learner-controlled environment designed according to the 4C/ID methodology (Van Merriënboer, 1997).

In this electronic environment, students have full control over the whole learning process, including the assessment of their own performance and the selection of learning tasks. Regarding the task selection process, students could select whatever task they want from a database with descriptions of all 45 genetics tasks available, which represent a combination of five difficulty levels (from low to high), three support levels (high, low and no support), and three different surface features (i.e., cover stories).

Method

In order to uncover which aspects students with different levels of prior knowledge consider when they engage in learner-controlled instruction, thinking-aloud protocol data were obtained from 60 students, who consecutively selected, performed and assessed five genetics tasks (Ericcson & Simon, 1993). After performing a learning task, students had to self-assess their performance on several criteria: accuracy of solution, process accuracy, perceived competence, perceived difficulty, invested mental effort, and perceived time-on-task (cf. Kostons et al., 2010). Besides prior knowledge level, self-efficacy beliefs may also influence students’ SDL skills (Zimmerman, 2000). To obtain this information, students’ self-efficacy beliefs were measured prior to each of the learning tasks with a rating of confidence in their prospective performance. A logging program will keep track of the time-on-task and the navigation of the participants through the learning tasks. Mental effort, as an index of cognitive load, will be measured during both practice and the test (after each task) with a 7-point rating-scale (Paas, 1992).

Expected Outcomes

Regarding the effects of prior knowledge on students’ SDL skills, it was hypothesized that high prior knowledge students would be more accurate in assessing their own performance, would make more use of their self-assessment scores for further task selections, and would more often choose tasks based on structural features (i.e., difficulty and support levels) than low prior knowledge students. Concerning the effect of self-efficacy beliefs on students’ SDL skills, it was predicted that students with high self-efficacy would select (and persist on) tasks that are challenging enough for them, even if they rate the accuracy of their solutions as low. However, because students’ self-efficacy beliefs change over tasks and time, we expected that self-efficacy would change depending on the characteristics of the tasks (i.e., difficulty levels), and as a part of learning process. The results indicate that high prior knowledge students more accurately assess their performance compared to low prior knowledge students, and their self-assessment accuracy as well as self-efficacy beliefs predict further task selections. However, high prior knowledge students did not consider different task aspects during task selections than low prior knowledge students. Further analysis is still in progress and these results will be presented at the ECER conference.

References

Corbalan, G., Kester, L., & Van Merriënboer, J. J. G. (2006). Towards a personalized task selection model with shared instructional control. Instructional Science, 34, 399-422. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (2nd ed.). Cambridge, MA: MIT. Knowles, M. (1975). Self-directed learning: A guide for learners and teachers. Chicago, IL: Follet. Kostons, D., Van Gog, T., & Paas, F. (2010). Self-assessment and task selection in learner-controlled instruction: Differences between effective and ineffective learners. Computers & Education, 54, 932-940. Mihalca, L., Salden, R. J. C. M., Corbalan, G., Paas, F., & Miclea, M. (2011). Effectiveness of cognitive-load based adaptive instruction in genetics education. Computers in Human Behavior, 27, 82-88. Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429-434. Van Gog, T., & Paas, F. (2009). Effects of concurrent performance monitoring on cognitive load as a function of task complexity. In N. Taatgen & H. Van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1605-1608). Austin, TX: Cognitive Science Society. Van Merriënboer, J. J. G. (1997). Training complex cognitive skills: A four component instructional design model. Englewood Cliffs, NJ: Educational Technology Publications. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82-91.

Author Information

Loredana Mihalca (presenting / submitting)

University of Koblenz-Landau

DFG-Graduate School Teaching and Learning Processes

Landau

Wolfgang Schnotz

University of Koblenz-Landau, Germany

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