Modern Modeling of Student Motivation and Self-Regulated Learning.

This examination of data selection preceding multivariate analysis compares results grained with "gentle" and "draconian" variable elimination. To acquire comparable results, two stages of statistical exploration into an integrated model of motivation, learning strategies, and quality of teaching were used. The goal of the paper was to select variables that were as powerful as possible and to use them to produce a new factor model, applying both standard linear (Statistical Package for the Social Sciences [SPSS]) and Bayesian (BAYDA) statistical software. The second stage of the. research, planned for the future, will involve efforts to produce sensible path models related to the'theory. The study of motivation, learning strategies, and teaching used research in a Finnish telecommunications company in which 156 respondents answered a questionnaire with 135 items. The statistical analyses used for variable selection are described. Research results indicate that both teaching style and subject material can influence study motivation and the use of learning strategies. Learning material can be described with respect to the degree that broad theoretical perspectives are developed, and the personal meaningfulness of the subject material or the drawing of logical conclusions from detailed evidence is emphasized. The new statistical methods described, especially the nonlinear approaches based on Bayesian networks, enable the application of statistical analysis to the study of student motivation and self-regulated learning. The SPSS syntax is attached. (Contains 19 tables, 6 figures, and 87 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONALRESOURCES INFORMATION CENTER (ERIC) Modern Modeling of Student Motivation and Self-Regulated Learning Petri Nokelain en Pekka Ruohotie This paper is prepared for the: Annual Meeting of the American Educational Research Association in New Orleans April 2000 2 BEST COPY AVAILABLE MODERN MODELING OF STUDENT MOTIVATION AND SELF-REGULATED LEARNING Petri Nokelainen and Pekka Ruohotie University of Tampere This paper is sequel to our long time comparison project' of linear and non-linear statistical methods. This time we want to examine exhaustively data selection process (part A of measurement model) preceding multivariate analysis, in our case, both factor and path analysis. Our goal is to compare results gained with 'gentle' and 'draconian' variable elimination. That path, chosen in the beginning, leads us to factor analysis and path analysis. In the end we tend to compare both solutions (model family's) with Bayesian evaluator. To forge comparable results we present two stages (the first and the second stage) of statistical exploration into integrated theoretical model of motivation, learning strategies (Ruohotie et al. 1996, pp.201 212) and quality of teaching (Kautto-Koivula, 1996, p.176; 1993, p.251). Our goal is to elaborate the original model of motivation and selfregulated learning (Kautto-Koivula, 1996) with same data set but new statistical methods. We refer to the original study with word "original" and our new attempts with word "optimized" (both first and second stage). The main goal for this paper is to select as powerful variables as possible and to use them to produce a new factor model (`first stage'). To reach the goal we apply both standard linear (SPSS [05]) and bayesian (BAYDA [03], BKD [04]) statistical software. The word "modern" in the paper title in this first stage stands for explorative and critical use of traditional linear statistical methods (especially on part A we spent several moments on studying preliminar prerequisities for the collected data). It also represents the use of Bayesian calculations for selecting variables and explaining their relationships. (Table 1.) The second stage, left to future study work, involves efforts to produce sensible path models confirmative to the theory with SEM programs like AMOS [02]. Models are constucted with the help of the first stage (exploratory) discriminant and factor analysis. The main purpose (and justification for the word "modern" on that second stage paper title, too) is to rank different models with bayesian software (EV) which compares applicant Bayesian Belief Networks to the reference network and predicts how many times more probable each applicant network is. We use EV on this paper to compare different variable occupations for factorial dimensions. Table I. The First and Second Stage Research Plan. Conditions for Learning Educational effectiveness cannot be assessed solely through the quantitative results of standardized tests. Intended outcomes may also be qualitative in nature. In that case efficacy is indicated by the quality of the learner's dedication as revealed by attributes (such as autonomy and self-direction) and demonstrated by the quality of consequences (such as the ability to apply concepts learned in new situations and in novel ways). In this case, the self-concept, motivation and self-regulating ability of the learner are critical determinants of success. (Ruohotie, 2000.) Although two learners may be equally motivated to learn they may differ in how they think about themselves, how they relate to a task and how they think about a goal. From this perspective, motivation is seen to be bound to specific cognitive processes such as information processing, metacognition and attribution. It is not to be understood, therefore, simply in quantitative terms of the effort expended or the time used to complete a task, but also in qualitative terms of what learners think about themselves, about the given task and about their performance. (Ruohotie, 1999.) They may have quite different underlying reasons for studying. For some, learning has inherent value. For others it is a means to gain extrinsic rewards. Because the meaning of a goal can vary significantly according to the context or thought processes which it elicits for different individuals, we cannot understand motivation purely in terms of goals presented to the learner. We must also ask why they process or attend to different information sources, why they value certain goals over others, which metacognitive processes they use while studying and how they evaluate and attribute their accomplishments in different learning contexts. Certain cognitive skills are typical of effective learners. Such learners succeed not simply because they have more knowledge, but because they have better skills for finding information and better information-processing Project NONE (Nonlinear Modeling in Social Sciences) with Henry Tirri (tirri @cs.helsinki.fi) and Tomi Silander (tsilande @cs.helsinki.fi), University of Helsinki, Finland. 2 Modern Modeling of Student Motivation and Self-Regulated Learning 25.04. 2000 strategies; that is, they have certain cognitive-motivational skills which are related to self-efficacy, attributions and selfregulation processes. (Zimmerman 1998). These skills can be developed and shaped through training in the same way as other cognitive and social skills. A learner's efficacy should, therefore, be assessed in relation to those beliefs, perceptions, interpretations and expectations which help him/her to perform independently and bind him/her to a task/studies as well as kindle belief and self confidence in learning. The efficacy of instruction should also be assessed in terms of its ability to develop, increase and stimulate specific cognitive-motivational processes. Knowledge is constructed by individuals. Information which has been perceived only becomes usable personal knowledge when it is reprocessed and integrated into the individual's existing knowledge structures. The learner's understanding of this process directs learning by influencing strategies of knowledge acquisition and thinking. Understanding of this process also directs a teacher's manner of instruction and the expectations that the teacher has for learners. The metacognitive or internal reflective abilities of the learner are of fundamental importance in all purposeful learning and the use and further development of cognitive skills is an integral part of the process. The pedagogical challenge is to bring the learner to recognize his/her own aims, and to see the learning process as a way of "taking control" of both external and internal circumstances. One central duty of the teacher is to awake in learners a willingness to ask why and how. The teacher is also a model for cognitive performance, a demonstration of how cognitive behaviour works. In reflective teaching the teacher is more a facilitator of learning than a distributor of knowledge. Effective learning is not only dependent on cognitive skill. The individual's selfconfidence and other personality traits also affect the extent to which new patterns and strategies of behaviour will be attempted and the degree to which the learner assumes personal responsibility and control. (Raustevon Wright, 1986.) Raustevon Wright and von Wright (1991) emphasize that the conditions for learning do not really change with advancing age. "Both the learning process and the conditions for learning are by nature the same from early years to old age. Learning to learn is a lifelong process but the earlier it is started the better the prognosis. " Motivation and Self-Regulation There is not yet a generally accepted overall theory of motivation that defines and unites all the factors in the motiv