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Impact of Moodle usage practices on students’ performance in the context of a blended learning environment Filippidi Andromahi , afilippidi@upatras.gr Nikolaos Tselios , nitse@ece.upatras.gr Vassilis Komis, komis@upatras.gr

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  1. Impact of Moodle usage practices on students’ performance in the context of a blended learning environment Filippidi Andromahi,afilippidi@upatras.gr Nikolaos Tselios, nitse@ece.upatras.gr Vassilis Komis,komis@upatras.gr University of Patras, Department of Educational Sciences and Early Childhood Education, Patras, Greece

  2. Plan presentation SALL 2010 • Background • Research objectives • Methodology • Results • Correlation between students’ activity and their performance • students’ activity explains 1/5 of their performance • Conclusions

  3. Background 1/3 SALL 2010 • During the recent years a significant LMS usage in higher education is observed • a series of important questions emerge related to • the study of the appropriate teaching methods, • the effective design of technological infrastructure • and the design of the interaction of students with the system • The last dimension depends largely on the earlier attitudes and perceptions of the learning community’s members like students and teachers

  4. Background 2/3 SALL 2010 • The usage of those technologies produces new teaching practices and paradigms such as • mixed models of traditional learning like blended learning, (Garrison & Kanuca, 2004) • the development of a mixed model of learning is a complex process of transforming an existing educational framework based on the following factors: (a) the context of the lesson (b) development of appropriate materials and resources, (c) adjustment of platform’s tools (d) support of active participation for all members of the learning community, while transforming the role of teachers to cooperators (C.Dalsgaard, and M.Godsk, 2007)

  5. Background 3/3 SALL 2010 • Referring to the students’ practices, according to R.Valle, and M. Duffy , 2009 expressed usage practices are concerned, 3 categories are distinguished: • mastery oriented users, • task focused users, • and minimalist in effort users • The usage patterns, are being studied through processing and meaning extraction of quantitative usage data, such as • total time of use, • usage spreading, • times pan across reconnection in the LMS system, • intensity of interaction • and amount of communication with other learners and/or teachers.

  6. Research objectives SALL 2010 • The goal of the study was to identify the factors that influence students’ performance while using Moodle, in the context of blended learning environment • The objectives of the study were • a) to investigate possible relations of students’ practices, as represented using a variety of interaction metrics, with their performance • b) to specify which of the students’ interaction metrics practices were strongly related to their performance

  7. Method of study SALL 2010 A case study approach was adopted, for the implementation of the research • 117 University students, participated in the study • Department of Educational Sciences and Early Childhood Education, University of Patras, • Academic year 2008-2009. • Materials and didactic approaches used in the course were gradually reformed previously • in order to be suitable with the adopted blended learning approach. • LMS used for this purpose was Moodle • available at http://150.140.160.60/moodle/course/view.php?id=30

  8. Procedure SALL 2010 • Course :"Information and Communication Technologies in Education" • The students attended a two-hour compulsory laboratory session for 11 consecutive weeks • Each laboratory session • dealt with a particular topic, related to the goals of the course • Afterwards, each student had to deliver a report related to the problem based assignment • During each lab the tutors • provided information about the topic and the goals of the session • subsequently explained each assignment given to the students • The materials provided to the students • were organized according to each topic • were available to the students until the end of the semester

  9. SALL 2010 Available services, resources and assessment model

  10. Design of the activities SALL 2010 • Design and delivery of the course was based on a social constructive pedagogical context • A number of principles of the blended learning were also adopted with respect to the goals and the context of the course • In addition, face to face characteristics and online technologies (Moodle) were utilized (D.R. Garrison, and H. Kanuka, 2004)

  11. Example of the LMS system The adopted pedagogical model was based on the problem based learning approach proposed by (Duffy, and J. Kirkley.,2004) SALL 2010

  12. Instruments SALL 2010 • Data collection • Moodle • used to collect participants’ log files and • a tool developed from our research group was used for data preprocessing of the user’s log files. • Analysis of data • SPSS (17.0) • data collected from the • users’ log actions, • the students worksheets’ grades and • their final examination, were encoded into categorical and numeric variables

  13. Results 1/5 SALL 2010 • From the analysis of the log files (86345 records in total), • the majority (79 out of 117, 67.5%) of the students, used the Moodle 3-4 times per week • It was also observed a significant variation, concerning the frequency of students’ access to the Moodle platform

  14. Results 2/5 SALL 2010 • A multiple regression was used to identify possible relation between the students’ performance and the variables expressing system usage • A total of 9 independent variables were considered as predictors for the multiple regression modeling • Usage frequency per week • Total • assignment views, • posts to the forum, • forum views, • glossary views, • questionnaire views, • resource views, • user views and user views all • The dependent variable included the following: • Final students’ grade

  15. Results 3/5 SALL 2010 • We assumed a linear relation between independent (predictor) and dependent (criterion) variables, • which means that we would expect either increase or decrease of the dependent variable caused by the increase of the independent one. • Regression coefficients (b-weights), which describe the relation between a predictor and a criterion, with a significance of p < 0.05 were considered

  16. Results 4/5 SALL 2010

  17. Results 5/5 SALL 2010 • The multiple regression analysis resulted in a model that explains 20.2% of the of students’ performance • Three out of nine independent variables were found to be significant correlated with the dependent variable, • F = 2.679, • p= 0.006< 0.01, • adjusted R2 =0.126. • The three independent variables, that found to be statistically significant were: • (a) moderate use, β = 0.389, p= 0.013< 0.05 • (b) questionnaire view, β= 0.255, p = 0.030 <0.05 and • (c) glossary view, β= 0.206, p=0 .049 <0.05

  18. Conclusions 1/2 SALL 2010 The recorded students’ interaction variables, describing Moodle usage, is correlated with the students’ performance Moderate use appeared as the most critical variable as a predictor in this study it was also revealed that the usage variables that also explain students’ performance are Questionnaire view and glossary view

  19. Conclusions 2/2 SALL 2010 • Students’ performance was significantly correlated to their system usage. • 3 variables mostly contributed to the correlation: • spectrum of use and specifically moderate use, • That indicates that students who used Moodle more intensively and more frequently also received higher grade • questionnaire and glossary inspection were important due to the problem solving learning environment nature of the requested projects • these results do not indicate a causal relationship, • they do indicate that the three independent usage variables were related to higher scores on the final students’ performance

  20. Future work SALL 2010 Future research goals are: • Further investigations in different contexts and levels of education are required for the generalization of results • In addition, further examination requires • the degree of contribution of each interaction’s characteristics • and perceptions to the students' learning outcome

  21. Thank you for your attention! AndromahiFilippidi,afilippidi@upatras.gr Nikolaos Tselios, nitse@ece.upatras.gr Vassilis Komis,komis@upatras.gr ICT in Education Group, University of Patras http://ecedu.upatras.gr/icte

  22. References SALL 2010 D.R. Garrison, and H. Kanuka, “Blended learning: Uncovering its transformative potential in higher education”, The Internet and Higher Education, vol. 7, May 2004, 95-105, doi:10.1016/j.iheduc.2004.02.001. C.Dalsgaard, and M.Godsk, “Transforming traditional lectures into problem-based blended learning: challenges and experiences. Open Learning”, The Journal of Open and Distance Learning, vol. 22, Feb. 2007, pp. 29-42, 10.1080/02680510601100143. T.Duffy,and J. Kirkley, Learner-Centred Theory and practice In Distance Education Cases From Higher Education, Mahwah, N.J.: Lawrence Erlbaum Associates, 2004. R.Valle, and M. Duffy, “Online learning: Learner characteristics and their approaches to managing learning”, Instructional Science, vol. 37, Mar. 2009, pp. 129-149, 10.1007/s11251-007-9039-0D

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