Modeling Critical Factors of Quality in e-Learning - A Structural Equations Model Test


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Rosario @icalt 2012, Rome 4-6 july

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Modeling Critical Factors of Quality in e-Learning - A Structural Equations Model Test

  1. 1. 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  
  2. 2. 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  
  3. 3. 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
  4. 4. 2.  Quality  in  e-­‐learning  as  a  mul:-­‐dimensional  concept  
  5. 5. 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.    
  6. 6. 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
  7. 7. 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.
  8. 8. 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.
  9. 9. 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
  10. 10. 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  
  11. 11. 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.    
  12. 12. 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
  13. 13. 4.  Empirical  research  
  14. 14. 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  
  15. 15. 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.    
  16. 16. 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.      
  17. 17. Slides available Rosário  Cação