Intervention Strategies to Increase Self-Efficacy and Self- Regulation in Adaptive On-Line Learning

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Intervention Strategies to Increase Self-Efficacy and Self- Regulation in Adaptive On-Line Learning Teresa Hurley National College of Ireland, Mayor Street, Dublin 1, Ireland teresa.hurley@gmail.com Abstract. This research outline refers to the validation of interventional strategies to increase the learner s motivation and self-efficacy in an on-line learning environment. Previous work in this area is mainly based on Keller s ARCS model of instructional design and this study argues for an approach based on Bandura s Social Cognitive Theory especially the aspects of self-efficacy and self-regulation. The research plan envisages two phases: The first phase will extract rules for interventional strategy selection from expert teachers. The second phase aims to validate these rules by providing to the learner the selected strategy and observing the resulting behavior. 1 Self-efficacy in Adaptive On-line Learning On-line education is one of the most dynamic and potentially enriching forms of learning that exists today. However, attrition rates for on-line learning courses, which tend to be 40% to 50% higher than traditional classroom courses (Dille & Metzack, 1991) is a serious problem resulting in personal, occupational and financial implications for both students and academic institutions. Motivation to learn is affected by a student s selfefficacy, goal orientation, locus of control and self-regulation. In a traditional classroom tutors can infer the level of motivation of a student from several cues, including speech, behavior, attendance, body language, or feedback and can offer interventional strategies aimed at increasing self-efficacy and self-regulation. Intelligent Tutoring Systems (ITS) need to be able to recognize when the learner is becoming demotivated and to intervene with effective motivational strategies. Such an ITS would comprise two main components, an assessment mechanism that infers the learners level of motivation from observing the learning behavior and an adaptation component that selects the most appropriate intervention strategy. This study aims to inform the development of the adaptation component by extracting and validating selection rules for motivational intervention strategies to increase learners self-efficacy.

2 Background 2.1 Learner Modeling Studies on the assessment of motivation in on-line learning to date include Del Soldato, 1994, de Vicente & Pain, 2002; 2003; Qu, Wang & Johnson, 2005; and Zhang, Cheng & He, 2003. These studies primarily focus on similar motivational states, mainly derived from the ARCS model (Keller, 1987a, 1987b), such as attention, relevance, confidence, and satisfaction. These states are inferred from behavioral cues in the interaction such as time taken, effort, confidence, and focus of attention. However, the theoretical basis for these models seems to be weak. We argue that a model of motivational states of learners should build upon a well established theory of motivation in learning. Accordingly, we propose a model that is based on Social Cognitive Theory (Bandura, 1986) and in particular the constructs of self-efficacy, goal orientation, locus of control and selfregulation. As learners differ widely in these constructs, intervention strategies must be adapted to suit the individual and the task. Such interventions may take the form of verbal persuasion, vicarious experience, mastery experience or scaffolding. 2.2 Motivation Motivation in general is defined as the magnitude and direction of behavior and the choices people make as to what experiences or goals they will approach or avoid and to the degree of effort they will exert in that respect (Keller, 1993). Pintrich and De Groot (1990) report that students with higher levels of intrinsic motivation and self-efficacy achieve better learning outcomes. Malone (1981) states that intrinsic motivation is created by three qualities: challenge, fantasy and curiosity. Social cognitive theory provides a framework for understanding, predicting, and changing human behavior. The theory identifies human behavior as an interaction of personal factors, behavior, and the environment. Self-efficacy. Bandura (1986) described self-efficacy as individuals confidence in their ability to control their thoughts, feelings, and actions, and therefore influence an outcome. Individuals acquire information to help them assess self-efficacy from (a) actual experiences, where the individual s own performance, especially past successes and failures, are the most reliable indicator of efficacy; (b) vicarious experiences, where observation of others performing a task conveys to the observer that they too are capable of accomplishing that task; (c) verbal persuasion, where individuals are encouraged to believe that they possess the capabilities to perform a task; and (d) physiological indicators, where individuals may interpret bodily symptoms such as increased heart rate or sweating as anxiety or fear indicating a lack of skill. Perceptions of self-efficacy influence actual performance (Locke, Frederick, Lee, & Bobko, 1984), and the amount of effort and perseverance expended on an activity (Brown & Inouye, 1978). Attribution Theory. Attribution Theory (Heider, 1958; Weiner, 1974) has been used to explain the difference in motivation between high and low achievers. Weiner identified

ability, effort, task difficulty, and luck as the most important factors affecting attributions for achievement. High achievers approach rather than avoid tasks relating to achievement as they believe success is due to ability and effort. Failure is attributed to external causes such as bad luck or a poor exam. Thus, failure does not affect self-esteem but success builds pride and confidence. Low achievers avoid success-related tasks because they doubt their ability and believe success is due to luck or other factors beyond their control. Success is not rewarding to a low achiever because he/she does not feel responsible, i.e. it does not increase his/her pride or confidence. Locus of Control. Locus of control (Rotter, 1966) is a relatively stable trait and is a belief about the extent to which behaviors influence successes or failures. Individuals with an internal locus of control believe that success or failure is due to their own efforts or abilities. Individuals with an external locus of control believe that factors such as luck, task difficulty, or other people s actions, cause success or failure. Goal Orientation. One classification of motivation differentiates among achievement, power, and social factors (McClelland, 1985). Individuals with a learning goal orientation (mastery goals) strive to master a particular task regardless of how many mistakes they make. Their primary goal is to obtain knowledge and improve skills. Individuals orientated towards performance goals are concerned with positive evaluations of their abilities in comparison to others and focus on how they are judged by parents, teachers or peers. It is possible for students to have learning and performance goals at the same time. Self-Regulation. Self-regulation refers to students ability to understand and control their learning by employing cognitive strategies that assist in construction of meaning and retention of information and by using metacognitive strategies such as planning and monitoring to control their progress (Zimmerman, 1994). 3 Research Question and Study Design This study aims to inform the development of the intervention component of an adaptive educational system. An assessment component that creates an accurate model of the motivational states of the learner is currently being developed in a related project being carried out by a fellow researcher and it is planned to use this assessment component in the validation stage of this study. The fact that this automatic assessment component has not yet been developed is currently a limitation for us. However, the intervention strategies will remain valid and can be incorporated as soon as the assessment component becomes available. In the meantime, a learner model will be created manually. With this model we propose to extract expert views on selection rules for intervention strategies that increase learners self-efficacy and self-regulation and these rules will be validated in a subsequent phase. The research will have two separate phases: The first phase will extract rules for strategy selection from experts. The second phase aims to validate these rules. A learner model has been developed based on the Social Cognitive Theory constructs of self-efficacy, goal orientation, locus of control, and self-regulation. The learner model

contains twenty-three learner personas which have been systematically developed using the above constructs. In order to identify rules to determine which interventional strategy - verbal persuasion, vicarious experience, mastery experience or scaffolding - is the most appropriate for each learner s persona when low motivation is observed from the behavior of the learner, the assistance of expert teachers will be sought. If, for example, a learner with low self-efficacy ( I believe I cannot pass this quiz ) and external locus of control ( The quiz items are too hard ) is involved, teachers might indicate that verbal persuasion ( This quiz is similar to the one that you just passed. Most students that passed the first quiz also passed the second. ) would be the strategy to adopt. In this way, a set of rules could be extracted. Second, these expert rules need to be validated in a real learning environment to see if the intervention strategies adopted actually increase the self-efficacy of the learner. This will take the form of a Wizard of Oz study where an intervention based on the extracted rules will be applied to a learner who has become demotivated. A human tutor will observe the learning behavior of students in an online course. Demotivated students will receive an intervention in accordance with the expert rules. The outcome will be assessed based on a subjective report from the learner (e.g., motivation and satisfaction) and on an observation of the learner s behavior and progress. In conclusion, this study offers a way to elicit and validate explicit rules from experts on intervention strategies to increase self-efficacy and self-regulation. These rules will then be used by an Intelligent Tutoring System to select the most appropriate intervention strategy for a demotivated learner and the results will be monitored. 4 Summary Social Cognitive Theory offers a theoretical framework for a deep knowledge of a learner s motivation by utilising the concepts of self-efficacy, goal orientation, locus of control and self-regulation as a base for interventional strategies to increase the level of the level of the learner s motivation. This research outline has been defined as the first step of a two-year project. A literature review has been conducted. The learner model is currently being developed which will be used to extract the rules and to validate the interventional strategies. Future work includes conducting the studies, analysing the data and drawing conclusions. It is anticipated that the first results will be available for presentation at the conference in June 2006. References 1. Bandura, A.: Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall (1986).

2. Brown, I. Jr., & Inouye, D. K.: Learned helplessness through modeling: The role of perceived similarity in competence. Journal of Personality and Social Psychology (1978) 36, 900-908. 3. De Vicente, A., Pain, H.: Informing the Detection of the Students Motivational State: an empirical study. In Cerri et al. (2002) 933-943. 4. De Vicente, A., Pain, H.: Validating the Detection of a student s Motivational State. In A. Mendez Vilas, J. A. Mesa Gonzalez, J. Mesa Gonzalez, editors, Proceedings of the Second International Conference on Multimedia Information & Communication Technologies in Education (m-icte2003), number 15 of "Sociedad de la Informacion" Series, Volume III, (2003) 2004-2008, Merida, Junta de Extremadura (Consejeria de Educacion, Ciencia y Tecnologia). 5. del Soldato, T.: Motivation in Tutoring Systems. PhD thesis, School of Cognitive and Computing Sciences, The University of Sussex, UK, 1994. Available as Technical Report CSRP 303 6. Dille, B., & Mezack, M.: Identifying predictors of high risk among community college telecourse students. The American Journal of Distance Education, (1991) 5(1), 24-35. 7. Heider, F. The Psychology of Interpersonal Relations. New York: Wiley. (1958). 8. Keller, J.M.: Strategies for stimulating the motivation to learn. Performance and Instruction, 1987a, 26 (8), 1 7. 9. Keller, J.M.: The Systematic Process of Motivational Design. Performance and Instruction, 1987b, 26 (9), 1 8. 10. Keller, J.M.: Motivational design of instruction. In C.M. Reigeluth (Ed.) Instructional design theories and models: An overview of their current status. Hillsdale, NJ: Erlbaum (1993). 11. Locke, E. A., Frederick, E., Lee, C., & Bobko, P.: Effect of self-efficacy, goals, and task strategies on task performance. Journal of Applied Psychology, (1984) 69, 241-251. 12. Malone, T.: Towards a theory of instrinsically motivating instruction. Cognitive Science, 4, 333-369. (1981). 13. McClelland, D.: Human motivation. New York: Scott, Foresman. (1985). 14. Pintrich, P.R., & De Groot, E.V.: Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, (1990) 82(1), 33-40. 15. Qu, L., Wang N., & Johnson, W. L.: Detecting the Learner s Motivational States in an Interactive Learning Environment. Artificial Intelligence in Education. C.-K. Looi et al. (Eds.), IOS Press (2005) 547-554. 16. Rotter, J. B.: Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs (1966) 80(Whole No. 609). 17. Weiner, B. (1974). Achievement motivation and attribution theory. Morristown, N.J.: General Learning Press. 18. Zhang, G., Cheng, Z., He, A., & Huang, T.: A WWW-based Learner's Learning Motivation Detecting System. Proceedings of International Workshop on "Research Directions and Challenge Problems in Advanced Information Systems Engineering", Honjo City, Japan, September 16 19, 2003, http://www.akita-pu.ac.jp/system/kest2003/ 19. Zimmerman, B. J.: Dimensions of academic self-regulation. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 3-21). Hillsdale, NJ: Lawrence Erlbaum (1994).