Learners’ Internal Management of Cognitive Processing in Online Learning Chun-Ying Chen Department of Electronic Commerce Transworld Institute of Technology, Taiwan AACE Conference 2010

2 Outline Introduction Introduction Deep level of cognitive processing and learning strategies Deep level of cognitive processing and learning strategies Purpose of the study Purpose of the study Methods Methods Results Results Discussion and Conclusions Discussion and Conclusions References References

3 Introduction Quality learning defined in this study Quality learning defined in this study  a deep level of cognitive processing Factors that impact quality learning in online discussions included: Factors that impact quality learning in online discussions included:  instructional methods  moderating strategies  course structure and leadership This study investigated: This study investigated:  learning strategies

4 Deep Level of Cognitive Processing and Learning Strategies Knowledge construction and deep level of cognitive processing Knowledge construction and deep level of cognitive processing Learning strategies Learning strategies  Cognitive strategies  Metacognitive strategies  Affective strategies

5 Cognitive Strategies aim at assisting learners’ cognitive processes to construct knowledge aim at assisting learners’ cognitive processes to construct knowledge Selection strategies Selection strategies Rehearsal strategies Rehearsal strategies Elaboration strategies Elaboration strategies Organizational strategies Organizational strategies Surface approaches Deep approaches

6 Metacognitive Strategies Are directed at regulating the cognitive and affective strategies Are directed at regulating the cognitive and affective strategies Several studies showed that metacognitive strategies lead to improvements in academic performance Several studies showed that metacognitive strategies lead to improvements in academic performance

7 Affective Strategies Refers to motivation, anxiety and fears of failure towards learning Refers to motivation, anxiety and fears of failure towards learning Studies showed that an absence of anxiety and intrinsic motivation contribute to deep processing (Entwistle & Waterston, 1988; Fransson, 1977) Studies showed that an absence of anxiety and intrinsic motivation contribute to deep processing (Entwistle & Waterston, 1988; Fransson, 1977)

8 Purpose of the Study to examine students’ strategy use to promote deep processing in asynchronous online discussion via CMC to examine students’ strategy use to promote deep processing in asynchronous online discussion via CMC

9 Research Questions What levels of cognitive processing (surface or deep processing) do students exhibit in their online discussion messages? What levels of cognitive processing (surface or deep processing) do students exhibit in their online discussion messages? What learning strategies do students exhibiting deep cognitive processing in online discussion use in comparison with students exhibiting surface cognitive processing? What learning strategies do students exhibiting deep cognitive processing in online discussion use in comparison with students exhibiting surface cognitive processing?

10 Methods Two Online Courses Two Online Courses  A course website + online discussion via computer conferencing  Similar learning activities:  participation and facilitation of online discussion  individual projects and critiques  final papers Participants Participants  12 graduate students from a college of education  Students with different ranges of experience with technology use and online courses were selected purposefully, considering students possessing varying degrees of online learning experience may apply different types of learning strategies.

11 Methods Data Sources Data Sources  Observations of online discussion  Semi-structured interview Data Analysis Data Analysis  Observations of online discussion  used Henri’s (1992) model for analyzing levels of information processing as the coding protocol  and then applied Newman et al.’s (1995) approach to convert each interviewee’s total counts of surface and deep processing into a depth of processing ratio for comparison  Semi-structured interview  The literature review regarding learning strategies was used in the development of the interview protocol  The interview data was transcribed and coded according to Chi’s (1997) method of analyzing qualitative data in an objective and quantifiable way

12 Credibility of the Findings Quantitative content analysis Quantitative content analysis  inter-rater reliability: Cohen’s kappa that accounts for chance agreement among coders  The kappa value was  0.83 for coding levels of information processing of conferencing transcripts  0.88 for coding the transcribed interviews quantitatively Interviews Interviews  extensive member checking  This study is limited to the context and setting of the two online courses, which need to be taken into consideration to make transferability judgments

13 Results Level of cognitive processing in online discussion Level of cognitive processing in online discussion  all students exhibited both deep and surface processing Group (n = 10) Total # of messages Total # of X + Total # of X - Average depth of processing ratio Range Deep (n = 5) ~1.00 Surface (n = 5) ~0.56

14 Results Differences in strategy use Differences in strategy use  students in the Deep group applied more strategies than students in the Surface group  students in the Deep group reported significantly more metacognitive and affective strategies  the Deep group did not report significantly more use of elaboration and organizational strategies, which are regarded as deep approaches to learning

15 Mean comparison of learning strategies between Deep and Surface groups Strategies Deep groupSurface group MeanSDMeanSDP Selection strategies NS Rehearsal strategies NS Elaboration & Organizational strategies NS Metacognitive strategies < 0.05 Affective strategies < 0.05 Note. The mean comparison and significance level are based on the Mann-Whitney U-test of two independent samples.

16 Discussion and Conclusions This study aimed to examine students’ internal management of cognitive processing in online learning via CMC. This study aimed to examine students’ internal management of cognitive processing in online learning via CMC. The results showed that The results showed that  they were all able to apply cognitive strategies to engage in deep learning.  Yet some participants seemed fail to sustain their engagement in deep learning and therefore posed more superficial messages in online discussion.  The findings further suggested that the deep processors used both metacognitive and affective strategies extensively, although did not report significantly more use of elaboration and organizational strategies that are regarded as deep approaches to learning, as compared to the surface processors

17 Discussion and Conclusions The surface processors tended to use the copying strategies to deal with the numerous ongoing messages in online discussion, and that results in the fragmentary understanding of the discussion contents. The surface processors tended to use the copying strategies to deal with the numerous ongoing messages in online discussion, and that results in the fragmentary understanding of the discussion contents. Reflected upon Bernt and Bugbee’s (1993) study, the surface processors in this study were those distance students who lack of metacognition, and that results in their failure to monitor their progress and their comprehension of materials. Reflected upon Bernt and Bugbee’s (1993) study, the surface processors in this study were those distance students who lack of metacognition, and that results in their failure to monitor their progress and their comprehension of materials. Affective strategies to maintain students’ motivation and concentration on learning are also important for students to engage in deep learning. Affective strategies to maintain students’ motivation and concentration on learning are also important for students to engage in deep learning.  Intrinsic motivation is another important affective factor for deep learning and may influence students’ choice of their strategy use.  Most students in this class had demands of either job or family responsibilities, so they had to organize their learning well and avoid distractions from the demands.

18 References Bernt, F.M., & Bugbee, A.C., Jr. (1993). Study practices and attitudes related to academic success in a distance learning programme. Distance Education, 14, 97–113. Chi, M.T.H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315. Entwistle, N., & Waterston, S. (1988). Approaches to studying and levels of processing in university students. British Journal of Educational Psychology, 58, 258–265. Fransson, A. (1977). On qualitative differences in learning: IV–Effects of motivation and test anxiety on process and outcome. British Journal of Educational Psychology, 47, 244–257. Henri, F. (1992). Computer conferencing and content analysis. In A.R. Kaye (Ed.), Collaborative learning through computer conferencing: The Najaden papers (pp. 115–136). NY: Springer-Verlag. Interpersonal Computing and Technology Newman, D.R., Webb, B., & Cochrane, C. (1995). A content analysis method to measure critical thinking in face-to-face and computer supported group learning. Interpersonal Computing and Technology, 3(2), 56–77.