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Horn Rothe Gersch - Which factors drive elearning usage?

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Information about Horn Rothe Gersch - Which factors drive elearning usage?
Education

Published on March 10, 2014

Author: HannesR1

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Hannes Rothe Department Business Information Systems Which factors drive e-learning usage? Anne-Marie Horn, Hannes Rothe, Martin Gersch Department Business Information Systems Presentation of a research paper at INTED 2014, March 10th 2014

2 I. Background (Educational Technology Acceptance) II. Discussion (Three hypotheses for e-learning usage) III. Empirical Study (Sample and empirical Results) IV. Conclusion Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Agenda Educational Technology Acceptance of Web-based Trainings

3 I. Background What is E-Learning? Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Ref. Illustration is based on Ebner et al. (2013) and Oliver Tomann | L3T Research Question: What affects students’ acceptance of information systems for e-learning in higher education?

4 II. Discussion Educational Technology Acceptance Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Hypothesis1: The perceived usefulnessis positively related to acceptances of e-learningsystems Hypothesis2: The perceived ease-of-useis positively related to acceptance ofe-learningsystems Hypothesis3: The perceived ease-of-useis positively associated with the perceivedusefulness Ref. Davis, Bagozzi & Warshaw (1989), Venkatesh and Davis (2000), Venkatesh and Bala (2008)

5 II. Discussion Influencing factors for ETA following a literature review Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Ref. Müller-Böling and Müller (1986), Stone (2005),Magoulas (2006),Chu (2010),Arenas-Gaitan et al. (2011), Sumak (2011),Kreidl (2011), Tarhini et al. (2013) I. Socio- demographic factors II. Personality traits III. Experience and prior knowledge IV. Attitude and interests - Age - Gender - Origin and Place of Study - Socio economical status - … - Cognition - Learning style and competence - Learning motivation - Locus of control - Self Efficacy - Computer anxiety - Computer Playfulness - „Big-Five“ - Achievement motivation - … - Domain knowledge - IT competence - Experiences with computers and the web - Experiences with educational technology - System-related - System knowledge - Usage success - Fun - Flow experience - … - Learning task - Learning satisfaction - IT affinity - Attitude towards e- learning - System-related - Task fit - Perceived value - Simplicity - System performance - Result demonstrability - Subjective norm - Trust - … Ref. Garff (2003),Jackson et al. (2005),Lang and Fries (2006), Magoulas (2006),Eom (2006), Rentroia-Bonito etal. (2006), Goodyear and Ellis (2007), Blackler et al. (2007),Aikman (2007), Gravill and Compeau (2008),Venkatesh (2008), Huber et al. (2008), Larsen etal. (2009),Sitzmann (2009),Bekele (2010),Fisher (2010), Orivs (2011), Hassanzadeh etal.(2012) Ref. Davis et al. (1982),Nielsen (1993), Csikszentmihalyi (1997),Goodhue (1995),Utz and Sassenberg (2001), Keramati et al. (2001),Jung (2002), Martins and Kelleramanns (2004), Kleimann etal. (2005),Mohs et al. (2006),Magoulas (2006),Wan et al. (2008),Sengpiel and Wandke (2008), Sengpiel and Dittberner (2008),Hessel (2009),Lee (2010), Sengpiel (2011), Traxler (2011) Ref. Davis et al (1989), Goodhue and Thompson (1995),Larsen etal. (2009), Venkatesh and Davis (2000),Ausburn (2004),Martins & Kelleramanns (2004), Schmidt(2005), Eom (2006),Bliue, Goodyear and Ellis (2007),Park and Wentling (2007),Hochholdinger and Schaper (2008),Sitzmann et al. (2009), Lee (2010), Bekele (2010), Traxler (2011),Hassanzadeh (2012),Tarhini et al. (2013)

6 III. Empirical Study Information Management Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe WBT Wiki Online discussion content communication with multiple mediaelements Web-based following designand educational patterns. Ref. Piccoli et al. (2001), Gabriel et al. (2009)

7 III. Empirical Study Sample Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Pseudonymized Data Collection Complete evaluations (n=344) 155 106 40 43 2009 2010 2011 2012 Gender distribution 71 52 17 21 70 40 21 20 2009 2010 2011 2012 male female Descriptives Age distribution 23,65 23,2 23,76 24,05 2009 2010 2011 2012 Key data Ref. Gabriel et al. (2006) Feedbackform

8 III. Empirical Study Measurement Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe Perceived ease-of-use of an e- learning system Ref. Davis et al. (1989), Šumak et al. (2011) Perceived usefulness of an e-learning system Acceptanceof an e-learning system Other factors a) Learning satisfaction (4 items; α=0,78, n=248) b) Behavioural intention a-priori (5 items; α=0,82, n=271) a-posteriori (1 item) (7 items; α=0,88, n=220)a) Unweighed (2 items; α=0,87, n=282) b) Weighed by importance of accessibility and simplicity (4 items) Scale: 1 (low) to 6 (high) Scale: 1 (high) to 6 (low) Scale: 1 (high) to 6 (low)

9 III. Empirical Study Results of parameter-free rang correlation analysis Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe General Perceived Usefulness (a-priori) General Perceived Ease-of-Use (a-priori) General Perceived Ease-of-Use (a-priori; weighed) τb p τb p τb p General Intention to Use (a-priori) .57** .000 -.28** .000 -.15** .000 Learning Satisfaction .27** .000 -.47** .000 -.10* .036 General Intention to Use (a-posteriori) .32** .000 -.18** .000 n.s. General Perceived Usefulness (a-priori) - -.26** .000 -.14* .001 * significant with p<.05; ** significant with p <.01; n.s. non-significant Perceived Ease of Use inversely polarized:Lowvalues equal high perceived ease-of-use ofe-learning. H1 H2 H3

10 III. Empirical Study Results of exploratory analysis Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe General Perceived Ease of Use (a-priori) General Perceived Ease of Use (a-priori; weighed) General Perceived Usefulness (a-priori) General Intention to Use (a-priori) General Intention to Use (a-posteriori) Learning Satisfaction Correlation τb p τb p τb p τb p τb p τb p Drop-out rate (key data) .09* .038 Drop-out rate (feedback forms) -.11* .021 -.10* .042 Time required for the WBT -.12** .008 I. Age -.09* .044 -.15** .000 -.12* .023 II. Intensity of Internet Usage .10* .027 .10* .027 -.10* .021 -.17** .000 III. Computer Skills .18** .000 .19** .000 -.17** .000 III. Diversity of E-Learning Usage .10* .047 .12* .019 III. E-Learning Knowledge -.20** .000 .19** .000 -.09* .038 -.19** .000 IV. Interest in E-Learning -.34** .000 .17** .000 -.37** .000 -.44** .000 -.33** .000 -.22** .000 ANOVA (cat. Variables) Value p Value p Value p Value p Value p Value p I. Year (F-Test) 4,352 .005 I. Sex (T-Test) 6,961, 000 .001 2,152 .032 I. Studies (F-Test) 4,629 .008 III. E-Learning Usage – private vs. studies (U-Test) 77,500 .032 166,000 . 030

11 III. Conclusion Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe H3: positive correlation H1: positive correlationH2: indecisive Perceived ease-of-use of an e-learning system Perceived usefulness of an e-learning system Acceptance of an e- learning system Summed up: Perceived usefulness is likely to have a more important influence on ETA But: There is a need for multivariate Analysis and inclusion of usage data

Hannes Rothe Department Business Information Systems Thank you for your attention.

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