Modeling Critical Factors of Quality in e-Learning - A Structural Equations Model Test
Modeling Critical Factors of Quality in e-Learning A Structural Equations Model Test ICALT 2012 –Rome, 4-6 July Rosário Cação Carlos Lucas de Freitas
Agenda 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
1. Introduc:on • Quality is one of the keys to business success and compe++ve advantage. • 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. We have tested, and confirmed, an existing model that represents the perception of quality in e-learning
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. Internaonal Journal of Informaon and Operaons Management Educaon, 3(4), 322-‐336.
3. A three-‐factor model of quality in e-‐learning Why have we used this model? • It is consistent with Jurans (1951) view of quality as ﬁtness for use • 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
4. Empirical research Purpose statement 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 The participantsCustomers of a Portuguese provider of 2741 answersasynchronous e-learning for professional 64% were womentraining, with ten years of experience in theconsumer e-learning market and anaccumulated count of over 60.000 clientsfrom 29 countries.The company offers around 200 short-termcourses ranging in length between 1 and 9weeks.
4. Empirical research The instrument A 1 to 10 scale, online survey, at the end of the courses, where 10 is the highest value. • 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
4. Empirical research The measurement model 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
4. Empirical research The measurement model 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 ulity 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 esmates because the measures revealed severe non-‐normality.
4. Empirical research The structural model Standardized regression weights Standardized paths of theVariable Training Training Training hypothesized model Process Attitude Utilities Hypo Causal Path Standardized - Path X6 0.786 thesis Coefficient X7 0.903 H1 Training process → perception of 0.42 X8 0.853 quality in e-learning X9 0.725 H2 Training attitudes → perception 0.33 X10 0.844 of quality in e-learning X11 0.832 H3 Training utility → perception of 0.22 X1 0.928 quality in e-learning X2 0.902 X3 0.584 X4 0.863 X5 0.899 X12 0.904 X13 0.917
4. Empirical research Model ﬁt CFA Structural model 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
5. Conclusions and future work • 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 ulies.
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. 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.
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