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Motivation, cognitive processing and achievement in higher education

by M Bruinsma
Learning and Instruction ()

Abstract

This study investigated the question of whether a student's expectancy, values and negative affect influenced their deep information processing approach and achievement at the end of the first and second academic year. Five hundred and sixty-five first-year students completed a self-report questionnaire on three different occasions. The departmental administrations provided data on the students' achievement. Covariance analysis indicated that student's expectancy and values positively affected the total number of credits. However, the expected relationship through the deep information processing approach was not found. Even though the analysis showed a relationship between students' expectancy, values and the deep information processing approach, this approach did not affect academic achievement.

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Motivation, cognitive processing ...

Motivation, cognitive processing and achievement in higher educationq Marjon Bruinsma* GION/Department COWOG, Centre for Research and Development in Higher Education, P.O. Box 2134, 9704 CC Groningen, University of Groningen, The Netherlands Abstract This study investigated the question of whether a student���s expectancy, values and negative affect influenced their deep information processing approach and achievement at the end of the first and second academic year. Five hundred and sixty-five first-year students completed a self-report questionnaire on three different occasions. The departmental administrations provided data on the students��� achievement. Covariance analysis indicated that student���s expectancy and values positively affected the total number of credits. However, the expected relationship through the deep information processing approach was not found. Even though the analysis showed a relationship between students��� expectancy, values and the deep information processing approach, this approach did not affect academic achievement. �� 2004 Elsevier Ltd. All rights reserved. Keywords: Learning Motivation Deep information processing Achievement Higher education q This article is based on a chapter of the doctoral dissertation ������Effectiveness of higher education. Factors that determine outcomes of university education������. * Tel.: C31 50 3637058 fax: C31 50 3636400. E-mail address: m.bruinsma@ppsw.rug.nl 0959-4752/$ - see front matter �� 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.learninstruc.2004.09.001 Learning and Instruction 14 (2004) 549���568 www.elsevier.com/locate/learninstruc
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1. Motivation, cognitive processing and achievement in higher education 1.1. Background of the study Over the years, educational researchers have examined the aspects that enhance academic achievement and prevent students from dropping out (for example, Bean, 1980 Bean & Metzner, 1985 Bijleveld, 1993 Jansen, 1996 Pascarella, 1980 Prins, 1997 Tinto, 1975, 1987). Many of these studies have shown that, apart from cognitive factors, motivation and emotion significantly influenced educational outcomes. What motivates students who are faced with a challenge to persist while other students decide to drop out? The question of how motivation facilitates learning and how it enhances performance has been an important point of departure in educational research over the past decades (for a review see Covington, 2000 Eccles & Wigfield, 2002). This article starts from an expectancy-value model of motivation (Eccles & Wigfield Jacobs & Newstead, 2000 Pintrich & De Groot, 1990 Wolters & Pintrich, 1998). In this model, motivation consists of three components, namely (a) an expectancy-component which concerns the student���s belief about his or her ability to perform the task or, in other words, it concerns the question ������can I do the task?������ (b) a value-component1 which refers to the student���s goals and beliefs about the importance and interest of the task or, in other words, ������why am I doing this task?������ and finally (c) an affective-component, which refers to the student���s emotional responses to the task, in other words, ������how do I feel performing this task?������. The basic assumption regarding the expectancy-component is that some students believe they can do a task and they believe that they are responsible for success in this task whereas others do not (Eccles & Wigfield, 2002 Pintrich & De Groot, 1990 Wolters & Pintrich, 1998). The relationship between expectancy and achievement may be mediated by such aspects as cognitive and meta-cognitive strategy use. More specifically, the students who believe that they are capable of performing a task tend to use more, and more appropriate, cognitive and meta-cognitive strategies. Furthermore, these students are more likely to persist in performing the task, (e.g. Tuckman, 1991 Vollmeyer & Rheinberg, 2000) resulting in higher levels of achievement. Various studies on the values, goals and beliefs about the importance and interest of a task have shown that goals influence school achievement through the quality, timing and appropriateness of various cognitive strategies (Covington, 2000). More specifically, the relationship between goals and achievement through cognitive strategy use might be either seen from a mastery goal perspective or a multiple goal 1 The conceptualisation of the value component in this model of motivation is primarily based on the work of Eccles & Wigfields. Other researchers have a different view of value and interest. For example Renninger, Ewen, and Lasher (2002) indicated that well-developed interest includes two interrelated components, stored knowledge and stored value, where stored value includes feelings of competence and positive and negative feelings that emerge when figuring out what is understood and what needs to be clarified. 550 M. Bruinsma / Learning and Instruction 14 (2004) 549���568
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perspective. The mastery goal perspective assumes that mastery goals, which increase one���s competency, understanding and appreciation for what is learned, are beneficial for learning whereas performance goals are not. The multiple goal perspective, on the other hand, argues that both mastery goals and performance goals might be beneficial for learning (Barron & Harackiewicz, 2003 Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002). Both perspectives assume that mastery learning goals favour deep level, strategic processing of information that enhances school achievement (Barron & Harackiewicz, 2003 Covington, 2000 Eppler & Harju, 1997 Jacobs & Newstead, 2000). Pintrich and De Groot (1990) stated that students with this motivational orientation, which involves goals of mastery, learning and challenge and the belief that a task is important and interesting, use more meta-cognitive and cognitive strategies and are more effective in their effort regulation. The differences between the two perspectives lie in their assumptions on the performance goals. The mastery goal perspective assumes that goals that involve performance or outperforming others result in superficial, rote- level processing (Covington, 2001). The multiple goal perspective, on the other hand, distinguishes between performance-approach goals or performance-avoidance goals these latter goals are usually linked with maladaptive outcomes (Harackiewicz et al., 2002). Related to the type of goal, studies have shown that the di���culty of the goals, or the level of task proficiency required, is important as well (see for example, Austin & Vancouver, 1996 Schunk, 1990). That is, the goal di���culty determines the amount of effort to attain a goal effort is greater when attaining di���cult goals. Studies on the affective-component have shown that various emotions influence both the quality of thinking and cognitive information processing (Meyer & Turner, 2002 Wolters & Pintrich, 1998). Positive emotions, such as curiosity, generally en- hance motivation and facilitate learning and performance. Negative emotions, like mild anxiety, can also enhance learning and performance by focusing the learner���s attention on a particular task (Hermans, as cited in Kuyper, van der Werf & Lubbers, 2000). However, intense negative emotions, like anxiety, panic, insecurity and related thoughts, such as feeling incompetent, generally adversely affect motivation, interfere with learning and contribute to a lower performance (Sarason, as cited in Kuyper, van der Werf & Lubbers, 2000). Over the years, academic emotions, i.e. emotions linked to academic learning, classroom instruction and achievement, with the exception of test anxiety and fear of failure, have largely been neglected in educational research (Pekrun, Goetz, Titz, & Perry, 2002). Recently, researchers have started to investigate other emotions that are, in addition to test anxiety, important predictors of learning outcomes (Pekrun et al., 2002 Sylwester, 1994). For example, Pekrun et al. showed that positive emotions such as enjoyment, hope and pride predicted high achievement, and negative emotions, such as hopelessness and boredom, predicted low achievement and that these emotions affected the decision to withdraw from university courses. The study also indicated that these emotions were, in addition to being associated with achievement, related to students��� motivation, learning strategies, cognitive re- sources and self-regulation. 551 M. Bruinsma / Learning and Instruction 14 (2004) 549���568

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