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Understanding medical students' self-regulated learning in traditional classroom and online learning context: a mixed method study with cluster analysis

Authors

  • Freman Chihchen Chou
  • Sheng-Chang Yang
  • Walter Chen
  • Hsiao-Chuan Lin

Theme

eLearning

INSTITUTION

China Medical University Hospital
China Medical University

Background

 

  • Self-regulated learning (SRL)? An argument that SRL is the interplay between learner and learning contexts rather than the traits of the learner only. (Greveson, 2005)

 

  • This study try to explore the  interplay  with 
     
    • Contexts?traditional classroom vs. online
    • Learners?participation patterns (clusters) among students; participation mediates the relationships between motivation, learning strategies and medical school performance.(Stegers-Jager, 2012)

 

 

  • SRL framework in this study
     
    • an active process of learning including being driven by motivational beliefs and adjusting the learning strategies of self-regulation. (Zimmerman, 1990) (Figure 1)

 

Summary of Work
  • Participants?300 , junior clerkship students, experienced the two contexts

 

  • Contexts?traditional and online contexts for knowledge acquisition complemented to clinical rotation

 

  • Measurements
    • 1. Students’ attendance in the two contexts
    • 2. Quantitative: SRL questionnaire (SRLQ) modified from OSLQ(Bernard, 2009) and MSLQ(Pintrich, 1990)
    • 3. Qualitative: Students’ feedbacks about how and why they perceived better SRL in which contexts

 

  • Analysis?
    • 1. Cluster analysis: with k-mean clustering (Aldenderfer, 1984) (Lin, 2011)
    • 2. Quantitative
      • Exploratory factor analysis (EFA) of SRLQ for both contexts
      • Comparisons between contexts and clusters
    • 3. Qualitative?Content analysis
Summary of Results

1. Clusters (Figure 2) (Table1 in detail)

Figure 2?The clusters of different participation patterns


2-1. EFA?revealed adequate validity and reliability with identical 4 factors in both contexts (Table2 in detail)

2-2. Compare SRL perceptions of traditional and online contexts among clusters (Figure 3) (Table3 in detail)


3. Content analysis?13 categories from 47 pattern codes, all 4 clusters can identify all the same 13 categories

  • All clusters recognized the facility to support SRL and the need of SRL for online context : more for online cluster
  • All clusters recognized the function of “regulated by others” for online context, especially acknowledged by the traditional cluster
  • However, few students around (4%) in each cluster thought: the SRL is independent from contexts
Conclusion
  • The interplay do exist! SRL is not pure learner trait and neither totally context dependent

 

  • The interplay is majorly constituted by “GT, Goal setting and Time management” and some by “SE, Self-Efficacy”

 

  • SE?the digital natives, the online cluster students are not so confident with traditional learning context

 

  • GT?most clusters of students perceived better SRL in online context, however the best in the traditional context become the worst in the the online context
Take-home Messages

online learning context may promote SRL with the consideration to recognize and help the traditional cluster of students.

Acknowledgement

The authors expressed their gratitude to Prof. Chih-Chung Tsai and Prof. Hsin-Kai Wu for their guidance about this research works. 

References

Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Newbury Park: Sage Publications.

Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12(1), 1-6.

Greveson, G. C., & Spencer, J. A. (2005). Self-directed learning - the importance of concepts and contexts. Medical Education, 39(4), 348-349.

Lin, C. C., & Tsai, C. C. (2011). Applying social bookmarking to collective information searching (CIS): An analysis of behavioral pattern and peer interaction for co-exploring quality online resources. Computers in Human Behavior, 27(3), 1249-1257.

Pintrich, P. R., & Degroot, E. V. (1990). Motivational and self-regulated learning components of classroments of academic-performance. Journal of Educational Psychology, 82(1), 33-40. 

Stegers-Jager, K. M., Cohen-Schotanus, J., & Themmen, A. P. N. (2012). Motivation, learning strategies, participation and medical school performance. Medical Education, 46(7), 678-688. 

Zimmerman, B. J. (1990). Self-regulated learning and academic-achievement - an overview. Educational Psychologist, 25(1), 3-17.

Background
Summary of Work
Summary of Results

Table 1

The clusters of different participation patterns (n = 238)

 

E-learning context

Traditional learning context

(1) Tradition cluster (n=70) mean/SD

31.53/14.91

81.61/16.14

(2) Active cluster (n=90) mean/SD

76.40/12.81

88.77/9.49

(3) Passive cluster (n=17) mean/SD

30.59/17.40

16.76/11.58

(4) Online cluster (n=61) mean/SD

67.31/15.82

42.74/14.89

F(ANOVA)

152.80***

253.45***

Post hoc tests (Scheffé tests)

 

2 > 1, 2 > 3, 2 > 4

4 > 1, 4 > 3

(2 > 4 > 1, 3)

1 > 3, 1 > 4

2 > 1, 2 > 3, 2 > 4

4 > 3

(2 > 1 > 4 > 3)

*** p < .001

 


 Table 2

Main factor loading of items and Cronbach’s alphas for each factor (n = 267)

Item

 

Online learning context

 

Traditional learning context

Self Efficacy

 

 

 

 

 

1

.824

 

.757

 

2

.760

 

.697

 

3

.621

 

.756

 

4

.806

 

.753

 

5

.734

 

.769

 

6

.775

 

.721

 

7

.688

 

.702

 

8

.663

 

.693

 

α

.920

 

.933

Goal-setting and Time-management

 

 

 

 

 

1

.682

 

.664

 

2

.777

 

.704

 

3

.639

 

.661

 

4

.828

 

.822

 

5

.797

 

.775

 

6

.740

 

.539

 

α

.926

 

.911

Task Strategies

 

 

 

 

 

1

.746

 

.650

 

2

.700

 

.804

 

3

.744

 

.562

 

α

.786

 

.785

Self-evaluation and Help-seeking

 

 

 

 

 

1

.715

 

.714

 

2

.866

 

.833

 

3

.818

 

.807

 

4

.600

 

.642

 

5

.732

 

.729

 

6

.673

 

.750

 

α

.896

 

.908

Overall alpha

 

0.95

 

0.96

Total variance explained (Varimax)

 

70.42%

 

70.43%

 


Table 3

The comparisons of perceived SRL factor among different clusters (n = 238)

 

Online

Traditional

SE

GT

TS

SH

SE

GT

TS

SH

(1) Tradition cluster (n=70) mean/SD

4.64/1.06

4.53/1.34

4.28/1.23

4.74/1.24

4.79/0.94

4.76/0.94

4.66/1.11

5.17/0.94

(2) Active cluster (n=90) mean/SD

4.81/0.85

5.15/0.91

4.68/0.94

5.19/0.86

4.75/0.89

4.66/1.03

4.58/1.03

5.06/0.96

(3) Passive cluster (n=17) mean/SD

5.02/0.91

4.66/1.16

4.12/1.21

4.77/1.32

4.27/1.23

3.95/1.20

3.88/0.93

4.68/1.16

(4) Online cluster (n=61) mean/SD

4.81/1.02

5.19/1.15

4.72/1.03

5.07/1.01

4.24/1.05

4.13/1.06

4.24/0.95

4.70/1.05

F(ANOVA)

0.90

5.49***

3.35*

2.64*

4.99**

6.47***

3.99**

3.16*

Post hoc tests (Scheffé tests)

 

 

2>1,

4>1

 

 

1>4

2>4

1>3, 1>4

2>4

 

 

 

* p < .05

** p < .01

 

*** p < .001


Conclusion
Take-home Messages
Acknowledgement
References
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