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Analysis of the Course Interest Survey in Distance Education

Analysis of the Course Interest Survey in Distance Education. Kevin E Kalinowski Department of Technology and Cognition August 3, 2006. ARCS Model. The ARCS Model is derived from the current literature on human motivation (Keller, 1987). Four established higher-order constructs: Attention

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Analysis of the Course Interest Survey in Distance Education

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  1. Analysis of the Course Interest Survey in Distance Education Kevin E Kalinowski Department of Technology and Cognition August 3, 2006

  2. ARCS Model • The ARCS Model is derived from the current literature on humanmotivation (Keller, 1987). • Four established higher-order constructs: • Attention • Relevance • Confidence • Satisfaction

  3. CIS • The CIS was designed with a theoretical foundation represented by the ARCS Model. • The Course Interest Survey (CIS) is a situational measure of students’ motivation to learn in a specific classroom setting (Keller, 1993). • 34 Question, Likert Scaled: • Not true • Slightly true • Moderately true • Mostly true • Very true

  4. Study • During Spring 2006, the CIS was given to three sections of CECS 1100 (Computer Applications) : • Face-to-face Classroom (n1 = 28) • Included for comparison • WebCT Control (n2 = 37) • WebCT Treatment (n3 = 54) • Several Simple Motivational Emails • All sections (ntotal = 119) taught by same instructor

  5. Confirmatory Factor Analysis • Classic goodness-of-fit indices (assumes S = Σ, but large n may be a problem) • Chi-square (X2) • Goal: X2 small enough so that pcalc > pcrit • Our Study: pcalc< .00001  • Absolute goodness-of-fit indices (also assumes S = Σ, but n is not taken into account) • Standardized root mean square residual (SRMR) • Goal: SRMR ≤ .08 • Our Study: SRMR = .12  • Parsimony correction indices (simple is better) • Root mean square error of approximation (RMSEA) • Goal: RMSEA ≤ .06 • Our Study: RMSEA = .13  • Comparative or Incremental fit indices (compare to null model) • Comparative fit index (CFI) • Goal: CFI ≥ .95 • Our Study: CFI = .88 

  6. Reliability • Cronbach’s coefficient alpha • Consistency of measurement

  7. Mean Plots for A, R, C, and S

  8. Mean Plot for ARCS

  9. Discussion • Our data do not fit the model well (but it could have been worse…) • Perhaps a larger sample would have helped? • Perhaps the model had too many dfs? • Or maybe the survey needs another go-round at EFA to reduce/reorganize questions… • The latent constructs continue to have a strong consistency of measurement. • Motivation was increased in the treatment group, and was on par with a face-to-face classroom.

  10. Conclusion • We (and others) argue that motivational communications are an important consideration for engendering a sense of community in distance education courses. • Adding several, simple motivational emails throughout the semester appears to increase motivation to levels consistent with a face-to-face classroom. • We plan on replicating this study this Fall with two more online sections of CECS 1100.

  11. Aside • On a personal note, • This has been a very interesting study with interesting data to work with, and • I found that CFA (like many statistical methods) is not an easy process with “real” data. 

  12. Analysis of the Course Interest Survey in Distance Education Kevin E Kalinowski kevski@unt.edu

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