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Rosario @icalt 2012, Rome 4-6 july
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Rosário Cação Carlos Lucas de Freitas
Modeling Critical Factors of Quality in e-Learning
A Structural Equations Model Test
ICALT 2012 –Rome, 4-6 July
1. Introduc+on 2. Quality in e-‐learning as a mul+-‐dimensional concept 3. A three-‐factor model of quality in e-‐learning
4. Empirical research • Purpose statement
• The par+cipants
• The instrument
• The measurement model
• The structural model: regression weights and path es+mates
• Model fit
5. Conclusions and future work
Agenda
• Quality is one of the keys to business success and compe++ve advantage.
1. Introduc:on
We have tested, and confirmed, an existing model that represents the perception of quality in e-learning
• There is no unique or widely accepted understanding of what quality is, and how to measure it and there is a lack of consensus about what quality in e-‐learning is.
2. Quality in e-‐learning as a mul:-‐dimensional concept
3. A three-‐factor model of quality in e-‐learning
Cação, R., & Figueiredo, A. D. d. (2010). Future u+lity as a key dimension in e-‐learning quality. Interna'onal Journal of Informa'on and Opera'ons Management Educa'on, 3(4), 322-‐336.
3. A three-‐factor model of quality in e-‐learning
• It is consistent with Juran's (1951) view of quality as fitness for use
Why have we used this model?
• It provides a long-term approach to the concept of quality, as it includes, not only effective uses or outcomes, but also expected uses
• It places the trainees at the center of the concept of quality and emphasizes their role in the construction of knowledge and quality
• It is based on the opinion of real customers, not on the opinion of experts, potential customers, or even on the researchers' opinion
• The final structure of the dimensions of quality was grounded on statistical evidence and on data reduction techniques
Purpose statement
4. Empirical research
To develop a measurement model and test a structural model made up of three constructs that affect the trainees'
perception of quality in e-learning: training process, training attitudes, and training utility
We have used structural equation modeling (SEM) to test the model with a new sample of data.
4. Empirical research
2741 answers 64% were women
The participants
Customers of a Portuguese provider of asynchronous e-learning for professional training, with ten years of experience in the consumer e-learning market and an accumulated count of over 60.000 clients from 29 countries.
The company offers around 200 short-term courses ranging in length between 1 and 9 weeks.
The instrument
4. Empirical research
• Global satisfaction • Fulfillment of expectations • Initial motivation • Final motivation • Fulfillment of training objectives • The platform and its functions • Training contents • The trainer’s expertise • The contribution of the forum for the learning process • The dynamics and help of the trainer in the forum • Competence, kindness, and promptness of the staff • Immediate professional utility • Future professional utility • Global quality perception
A 1 to 10 scale, online survey, at the end of the courses, where 10 is the highest value.
The measurement model
4. Empirical research
The measurement model included three constructs represen+ng latent variables:
• Training process included beliefs the trainees have toward the day-‐to-‐day of the course
• Training a1tudes represented reac+ons and beliefs the trainees have towards the training course
• Training u3lity was the extent to which the trainees feel that the course will have impact on their personal and professional life, considering both the short and the long term
The measurement model
4. Empirical research
We have defined a recursive model with the following hypothesized structural rela+onships:
H1: Training process is posi+vely related to the percep+on of quality in e-‐learning H2: Training a>tudes are posi+vely related to the percep+on of quality in e-‐learning H3: Training u'lity is posi+vely related to the percep+on of quality in e-‐learning
We have followed a two-‐step SEM process, i.e., we have tested first the fit and the construct validity of the measurement model (Hair et al, 1992, pp. 717-‐718).
The es+ma+on technique used was the scale-‐free least squares es'mates because the measures revealed severe non-‐normality.
The structural model
4. Empirical research
Variable Training Process
Training Attitude
Training Utilities
X6 0.786 X7 0.903 X8 0.853 X9 0.725 X10 0.844 X11 0.832 X1 0.928 X2 0.902 X3 0.584 X4 0.863 X5 0.899 X12 0.904 X13 0.917
Standardized regression weights
Hypo-
thesis
Causal Path Standardized Path
Coefficient H1 Training process → perception of
quality in e-learning 0.42
H2 Training attitudes → perception of quality in e-learning
0.33
H3 Training utility → perception of quality in e-learning
0.22
Standardized paths of the hypothesized model
4. Empirical research
Model fit
4. Empirical research
CFA model
Structural model
Chi-‐square (χ2) Chi-‐square 29.941 30.716 Degrees of freedom 62 72
Absolute fit measures Goodness-‐of-‐fit index (GFI) 0.996 0.996 Root mean square residual (RMR) 0.121 0.114
Incremental fit indices Normed fit index (NFI) 0.995 0.996 Rela+ve fit index (RFI) 0.994 0.995
Parsimony fit indices Parsimony normed fit index (PNFI) 0.791 0.788 Adjusted goodness-‐of-‐fit index (AGFI) 0.994 0.995
• Our research enabled us to confirm a model of quality in e-‐learning composed by three factors.
• According to this model, the percep+ons of quality in e-‐learning can be explained, with comfortable goodness-‐of-‐fit, by three factors: the training process, the training a>tudes, and the training u'li'es.
5. Conclusions and future work
The model provides e-‐learning companies with a conceptual framework to beeer understand what quality is.
• It makes clear that improving quality implies working on two dis+nct and addi+onal areas, besides the training process;
• it emphasizes the valua+on of the training outcomes, as well as the role of the trainees' agtudes in the construc+on of the perceived quality.
5. Conclusions and future work
The model can also be used to classify and organize the mul+ple dimensions of quality proposed in the literature and to determine specific courses of ac+on intended to improve quality percep+ons in a specific factor of quality.
