In Global Peace Through The Global University System

2003 Ed. by T. Varis, T. Utsumi, and W. R. Klemm

University of Tampere, Hameenlinna, Finland

 

 

PRACTICAL CONSIDERATIONS OF MOTIVATION AND COMPUTER-SUPPORTED COLLABORATIVE LEARNING

 

 

Pekka Ruohotie and Petri Nokelainen

University of Tampere

 

 

Introduction

 

The first chapter in this book by Utsumi, Varis and Klemm inspired us when pondering how recent achievements in the field of motivational research could benefit a Global University System. GUS will use Internet, which is less expensive and has the potential to be global as compared with the regional coverage of satellite. Another benefit of Internet-based teaching is that it can be totally asynchronous, enabling more interaction among/between students and instructors compared with classroom teaching via satellite.

 

The lead chapter authors state that it is important to "coordinate the delivery of content and rich educational experiences leading to a GUS degree" when aiming at quality education. Further, GUS "will include the most up-to-date research and methods, the most recent developments and insights in its various fields of study, and will be supported and enhanced by the latest advances in communication technology."

 

Expectations for successful learning processes in computer-supported collaborative learning (CSCL) environments within GUS are set high. As our expertise is mainly on the field of applied motivational research (see Ruohotie, 2002; Nokelainen and Ruohotie, 2002), this chapter concerns motivational issues and their relationship to certain features in computer-supported collaborative learning environments that we have found important when conducting several empirical studies (see Nokelainen et al., 2003; Nokelainen, Ruohotie, et al., 2003).

 

Our chapter discusses first the relationship between learner-centered learning and motivation. Secondly, we will argue for the use of self-rated questionnaires in distance education. Finally, summary of main results of two independent case studies is presented.

 

 

Learner-centered Learning

 

The learner-centered learning principles (APA, 1997) present a shift from the traditional teacher-centered approach to learner-centered approach of instruction providing learner with valuable real-life skills (Dillinger, 2001). Motivational and affective factors play a significant role on the list consisting of motivational and emotional influences on learning, intrinsic motivation to learn, and effects of motivation on effort (APA, 1997). However, practical implications that benefit network learners are difficult to conduct as these factors are presented in broad and abstract level.

 

Specific form of constructivist theory is under continuous debate, but researchers agree (see Bonk and Cunningham, 1998; Dalgarno, 2001; Leflore, 2000; de Vries, 2001) that following characteristics are included: learner construction of meaning (von Glasersfeld, 1995); social interaction to help students learn (Vygotsky, 1978); and student problem-solving in "real world" contexts (Duffy and Jonassen, 1991).

 

Bonk and Cunningham (1998, 32) suggest that we are able to examine motivational issues, such as meaningfulness of studies and self-regulation of learning (Ruohotie, 2002), in more detailed level from the constructivistic point of view. This thought is supported also by Leflore (2000) who argues, with implemented list of guidelines (p. 113-115), that constructivist approach offers a suitable theoretical base for Web-based learning. As GUS aims to provide both high quality and cost-effective learning opportunities (Utsumi, Varis and Klemm, 2003), needs of diverse learner populations should be taken into account.

 

Nokelainen and Tirri (2002) argue "learner could be provided with valuable self-regulatory information making learning personally relevant and meaningful task by entering information about his/her personal motivation via questionnaire in the beginning of the learning process." They suggest that if this personal information is updated in regular intervals, the learner is able to see the development in her own motivational profile over time and compare it to the average profile values of other learners. Further, learning environment could provide personalized links to resources for profiled users, based for example, on the level of their intrinsic motivation to learn current topic.

 

Pintrichπs motivational expectancy model (1988) categorizes and integrates the central elements of modern motivational theories. This model includes different beliefs or expectancies; for example, perceived competence, test anxiety, perceptions of task difficulty, the learnerπs belief in his/her efficacy, and expectancy of success. The learner who has a strong self-image and high expectations will put more effort into his/her task and will persist longer, even on a difficult task, compared to the student who has low expectancy of success.

 

A value perspective is evident in the evaluations of the course/task value as well as in the studentπs goal-orientation. The course/task value has three facets: attainment value, interest value and benefit value. The attainment value refers to the degree of challenge the learner anticipates. It is high if a learner feels him/herself capable and estimates him/herself to be able to master demanding course work. Interest value refers to a learnerπs intrinsic interest in the contents of a certain subject (e.g., he/she likes chemistry). The utility value refers either to the goal itself or to the instrumental task. For example, a learner who is not interested in chemistry may nonetheless study it enthusiastically because chemistry is a compulsory part of the school program. The course then has a high utility value. The material to be learned could well have utility value as well as interest value for the learner but, while it is desirable, it is unrealistic to expect that instruction will always increase a studentπs intrinsic interest towards learning.

 

In his several articles Pintrich has described the components of motivation and their role in learning (e.g. Pintrich, 1988; 1995; 2000; Pintrich and McKeachie, 2000; Pintrich and Schrauben, 1992). His own studies show that different motivational components, such as self-efficacy, internal goal orientation and test anxiety clearly correlate with the use of cognitive and metacognitive strategies (see Pintrich et al., 1991; Pintrich and Schrauben, 1992).

 

 

Assessing Learners' Self-rated Motivation

 

It is obvious that in order to understand complex phenomenon such as student motivation, both multi-faceted theoretical and methodological approach is required. Unfortunately, severe limitations for data collection exist in the context of computer-supported learning:

 

• Teacher (or tutor) has few face-to-face discussions with the students. In fact, as an expert in certain area, he/she has to deal each time with a new group of attendees. Furthermore, study processes including collaborative actions, such as group work, are conducted through the distance learning platforms.

 

• Personal face-to-face tutoring time for each student is very limited and leads to situation where the guidance is conducted via synchronous (e.g., chat) or asynchronous (e.g., email) tools.

 

Under these circumstances GUS teacher or tutor should make good use of all the available information channels in order to understand the personal needs of each student. Paul Pintrich and his colleagues (Pintrich et al., 1991) developed the Motivated Strategies for Learning Questionnaire (MSLQ) to assess learners' self-rated motivation and cognition in the classroom.

 

As the MSLQ was targeted for measuring college student learning, Finnish researchers developed the first adaptation, Motivation and Learning Strategies for Adult Education, for the field of vocational education in the "Growth Needs" project during 1992-1994 (Ruohotie, 1994). The first version of the instrument, with 40 items measuring motivational scale, was tested with both adult learners of several companies acting in Finnish business sector and adolescent learners of vocational education institutes. Total sample size of this initial study was 156 respondents (Ruohotie, 1994).

 

The latest version, Abilities for Professional Learning (Ruohotie, 2000), still retains the same basic structure of the MSLQ measuring factors based on motivational expectancy model (Pintrich 1988). The questionnaire has versions for both vocational and university students. (Table 1.)

Table 1. Elaboration of the motivational scale for professional learning

 

The total number of items in the APLQ is 116 as the instrument measures four dimensions of professional learning: (1) learning experiences and motivation, 28 items; (2) study habits, 40 items; (3) quality of teaching, 23 items; and (4) effects and outcomes of education, 25 items.

 

Motivation category of the APLQ has three sections: (1) a value section, (2) an expectancy section and (3) an affective section. The value section has three subscales: (1.1) intrinsic goal orientation, (1.2) extrinsic goal orientation, and (1.3) meaningfulness of study. The expectancy section consists of two subscales: (2.1) control beliefs and (2.2) self-efficacy. The affective section includes one component: (3.1) test anxiety.

 

Next we present the results of two independent case studies. In both of these studies we have first measured students self-rated motivation level with Abilities for Professional Learning Questionnaire (Ruohotie, 2000), and then embedded the profile information into a CSCL environment. Our purpose in these case studies was to examine how learners use motivational profile information, in this case a group code given to each learner, when selecting partners for group work. We were also interested to see how a priori motivational profile predicts learning outcomes, in this case course mark.

 

 

Applying Motivational Profile Information into CSCL

 

Case Study 1

 

In the first case study studentsπ motivational profile information was embedded into a collaborative learning environment, EDUCO (Kurhila, et al., 2002). Information about studentsπ motivational level was collected in the beginning of a web-based university-level course with APLQ (Ruohotie, 2000) containing 28 items. Respondents (13 female and 31 male under-graduate students, n=44) median of age was 25 years. Information was collected with an on-line questionnaire system, EDUFORM (Nokelainen, et al., 2001).

 

Figure 1 presents the EDUCO "Map" and "Chat" views. Map view on the left-hand side is a two-dimensional graphical visualization of the course content consisting of icons (web pages) and dots (users on-line) attached to them. The color of a dot indicates a group membership that derives from the self-rated motivational profile information.

 

 

Figure 1. The "Map" and "Chat" views of a CSCL environment (EDUCO).

 

Based on the motivational level scores on APLQ, respondents were divided into three groups:

1. Blue group: Extrinsic goal orientation, test anxiety and meaningfulness of studies.

2.     Green group: Efficacy beliefs, intrinsic goal orientation and meaningfulness of studies.

3. Red group: Control beliefs and intrinsic goal orientation.

 

Students were asked to form weekly a different group of two and produce essays on the basis of 50 scientific documents included in the system. All the documents were published in the Internet by various authors.

 

The preliminary results with the empirical sample were as follows:

 

• Self-rated motivation had no direct relationship neither to study success (average score of seven weekly assignments) nor synchronous features of the system (use of chat channel or viewing the same documents that other users are currently viewing). The result was unexpected at least in former case. We conducted in another study setting comparative analysis with self-rated learning strategy and social ability profiles and found that both studentsπ social ability and learning strategy profiles had various connections to study success, but only latter was related to the use of synchronous features. Our previous studies with larger samples have shown that at least half of the items measuring motivational level are positively correlated with cognitive learning outcomes (Ruohotie and Nokelainen, 2000).

 

• Self-rated motivation had a positive relationship to both number of comments and newsgroup messages user has written. The number of documents visited during the course was found to be the only viable predictor for the final examination score.

 

• Most of the students used color information based on motivational profiling as a second opinion when choosing group mate. Those who reported that they did not check any profiles were less satisfied with their group mates.

 

Case Study 2

 

The second case study investigated the process of employing in-service teacherπs self-rated motivation profile information into collaborative learning tasks of an on-line learning environment. Main focus is to study how profiling information (Miettinen, et al., 2002) is related to various tasks (such as on-line group formation, peer-to-peer highlighting and commenting of the course material) performed by adult learners in a computer supported collaborative learning system.

 

Information about motivation was collected with an on-line questionnaire system, EDUFORM (Nokelainen, et al., 2001), in the beginning of a web-based university-level statistics course in Fall 2002. The sample consisted of 37 female and 17 male Finnish vocational education in-service teachers (N=54) conducting their post-graduate degree. The respondentsπ median of age was 36 years.

 

Motivational profile information was embedded into the EDUCOSM (Miettinen, et al., 2003) system. The system consists of a set of tools (i.e., "Search", "Newsgroups" and "Filters") for asynchronous collaborative knowledge constructing. The system appears to the user as a button bar at the top of the browser window and a custom popup menu that is available on any page being accessed through the system (Figure 2). The button bar is for navigating between the various views, including desktop, search and filter creation views. Functions for handling individual documents are located in the popup menu allowing the students to add new material to the system, create annotations (i.e., highlight and comment documents) and establish newsgroups.

 

The main research findings were as follows:

 

• Annotation quality in the learning tasks was rated low on those students who like practical instructions from teacher.

 

• Annotation quality was rated high on those students who are interested in the course topic and generally prefer to study demanding subjects from which they can learn something new.

 

• Students who are nervous in test situations had lower scores than those who need performance related feedback from teacher.

 

• All the respondents strongly agreed when asked "if the system brought added value to the learning process" and "if it changed their studying habits favourably" (when compared to the traditional university lectures).

 

• All the learners would recommend the system (i.e., highlighting and commenting documents) for other courses, too.

 

• Both self-made highlightings and comments were experienced to be more useful for the learning process than those made by other learners. This finding needs further investigations with larger empirical samples.

 

• Respondents made no distinction between anonymous and full name annotations. We expected that learners would be more relaxed when annotating anonymously and thus this research finding should be verified or falsified with other samples in the future.

 

 

Figure 2. EDUCOSM user interface with one highlight ("... correlation between the two halves of the scale ... ") and comment ("... Spearman-Brown split half ...").

 

 

Conclusions

 

In this chapter we have discussed relationship between learner-centered learning and motivation. We have suggested that using self-rated questionnaires for self-regulative purposes is one viable way to promote learner-centered learning in GUS. We presented summaries of two independent case studies. In our study setting the role of instructor was to provide an orientation to the topic through theoretical face-to-face lectures. We also gave few pointers to selected on-line resources. The system provided tools to process information and collaborate with peer learners. The role of a learner was to construct knowledge in collaboration with other learners in real-life distance learning situations. The major finding was that when aiming at successful computer-based learner-centered learning, learners should take responsibility for their own learning. We believe that this is in harmony with modern psychological and educational theoretical perspectives based on the assumption that a learner is an active contributor in the individual learning process (Snow, et al, 1994). Our research interest lies on learner motivation, but the design of two presented case studies is feasible for other target domains.

 

 

References

 

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de Vries, E. (2001). Hypermedia for Physics Learning: The Case for the Energy Concept. In J. Rouet, J. Levonen & A. Biardeau (Eds.), Multimedia Learning ≠ Cognitive and Instructional Issues, 141-153. Oxford: Pergamon.

 

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Kurhila, J., Miettinen, M., Nokelainen, P., & Tirri, H. (2002). EDUCO - A Collaborative Learning Environment Based on Social Navigation. In Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems (pp. 242-252).

 

Leflore, D. (2000). Theory Supported design Guidelines for Web-Based Instruction. In B. Abbey, (Ed.), Instructional and Cognitive Impacts of Web-Based Education, 102-117. Hershey: Idea Group Publishing.

 

Miettinen, M., Kurhila, J., Nokelainen, P., FlorÈen, P., & Tirri, H. (2003). EDUCOSM - Personalized Writable Web for Learning Communities. Proceedings of the ITCC 2003 Conference, (pp. 37-42). Las Vegas, USA.

 

Miettinen, M., Nokelainen, P., Kurhila, J., Silander, T., & Tirri, H. (2002). Adaptive Profiling Tool for Teacher Education. Society for Information Technology and Teacher Education International Conference, Vol. 2002, 1, 1153-1157.

 

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Pintrich, P.R., & McKeachie, W.J. (2000). A Framework for Conceptualizing Student Motivation and Self-Regulated Learning in the College Classroom. In P.R. Pintrich & P. Ruohotie (Eds.), Conative Constructs and Self-Regulated Learning. University of Tampere: Research Centre for Vocational Education.

 

Pintrich, P.R., & Schrauben, B. (1992). Studentsπ Motivational Beliefs and Their Cognitive Engagement in Classroom Academic Tasks. In D. Schunk & J. Meece (Eds.), Student perceptions in the classroom. Hillsdale, NJ: Lawrence Erlbaum, 149≠183.

 

Pintrich, P.R., Smith, D., Garcia, T., & McKeachie, W.J. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire. Technical Report 91-B-004. The Regents of The University of Michigan.

 

Ruohotie, P. (1994). Motivation and Self-regulated Learning. In P. Ruohotie & P. Grimmett (Eds.), New Themes for Education in a Changing World, (pp. 15-60). Career Education Books: Saarij‰rvi.

 

Ruohotie, P. (1998). Motivated Strategies for Professional Learning. University of Tampere: Research Center for Vocational Education.

 

Ruohotie, P. (2000). Abilities for Professional Learning. University of Tampere: Research Center for Vocational Education.

 

Ruohotie, P. (2002). Motivation and Self-regulation in Learning. In H. Niemi & P. Ruohotie (Eds.), Theoretical Understandings for Learning in the Virtual University, (pp. 37-72). Research Centre for Vocational Education, University of Tampere.

 

Ruohotie, P., & Nokelainen, P. (2000). Modern Modeling of Student Motivation and Self-regulated Learning. In P. R. Pintrich and P. Ruohotie (Eds.), Conative Constructs and Self-regulated Learning, (pp. 141-193). University of Tampere, Research Centre for Vocational Education.

 

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Author Biographical Sketches

 

Pekka Ruohotie, PhD. Professor of Career Education Director of the Research Centre for Vocational Education and Training University of Tempere P.O. Box 229 FIN-13101 H‰meenlinna, Finland Tel. +358 3 61451 E-mail: pekka.ruohotie@uta.fi Web: http://www.uta.fi/laitokset/aktk/

 

Pekka Ruohotie, PhD., is a Professor of Career Education, University of Tampere. Professor Ruohotie is the director of the Research Centre for Vocational Education and Training (RCVE). He is the head of the research department of various large-scale companies. He was during 1992-1993 a Visiting Professor at Simon Fraser University. During 1996-2000 Professor Ruohotie was a member of the Finnish Higher Education Evaluation Counsil (FINHEEC) and the Dean of the Faculty of Education at Tampere University. He has been since 2000 the chairman of the Adult Education Council of Finland. He is Editor-in-Chief of the Finnish Journal of Vocational Education. Professor Ruohotie has written numerous textbooks, articles and chapters on professional growth and development that are widely used both in business and educational science domains. His current research interest lies on the study of motivated strategies for professional learning and self-regulation in learning.

 

 

Petri Nokelainen, EdLic. University of Tampere P.O. Box 229 FIN-13101 H‰meenlinna, Finland Tel. +358 3 61451 E-mail: petri.nokelainen@uta.fi Web: http://www.uta.fi/laitokset/aktk/

 

Petri Nokelainen, EdLic., is as a Research Scientist in the Research Centre for Vocational Education and Training (RCVE), University of Tampere. He is responsible for research on applied statistical methods. He is a visiting Research Scientist in the Complex Systems Computation Group (CoSCo), University of Helsinki. He develops tools for evaluating and testing of digital learning environments. His special research interest lies on the study of modern network-based learning.