Interaction With Print-Learning Materials And Academic Performance And Persistence Of New Students Of Universitas Terbuka (The Indonesian Open Learning University)

Hardhono, Anthonius Padua (1994) Interaction With Print-Learning Materials And Academic Performance And Persistence Of New Students Of Universitas Terbuka (The Indonesian Open Learning University). Doctoral thesis, University of Victory.

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Abstract

In a distance education setting, student individual interaction with print learning material (modules) comprises the largest part of student learning, and the interaction may be an essential ingredient in student academic performance and student persistence. To investigate this relationship, the reader-text relationship construct was used as a base for developing the construct of student interaction with modules. Ten factors constituted the interaction: Individual Activities, Time in Interaction, Study Motivation, Understanding of Directions,Difficulties in Interaction, Reading Speed, Study Load, Learning by Memorizing, Learning by Understanding, and Attitudes After Interaction. An instrument was developed to measure this interaction. Universitas Terbuka (UT}, the Indonesian Open Learning University, was chosen as the research site. UT has been characterized both by low student academic performance and by high non-persister rates of new students. As modules have been developed to maximize student academic performance, the low academic performance and high non-persister rates suggest either that students did not follow the prescribed methods of studying the modules or that they have had difficulties in studying the modules. It seemed, therefore,worthwhile to conduct a study on how students interact with modules at UT. The main purpose of this study, therefore, was to examine the student individual interaction with modules and its relationship with academic performance and persistence. Reviews of related literature showed that many factors are related to academic performance and persistence. This study, in addition to 10 factors of interaction with modules, included two contextual factors: previous academic performance and time that students spent at work and on their way to/from work. This study also focused on one population, namely first semester students who were taking EKON4110, the Introduction to Macro Economics. The data gathering was conducted in two stages approximately 7 months apart and the instrument contained both quantitative and qualitative components. In the first data gathering, May, 1993, an instrument was sent to all 317 first semester students who were taking EKON4110. There were 156 students who returned the instrument. The second data gathering was conducted both to examine the stability of responses over a 7-month period and to gather data on why students registered and/or did not register in the second semester. In the second data gathering, the instrument was sent to 43 students who registered in the second semester and to 42 students who did not register. Eighteen students from the former and 10 students from the latter returned the instrument. The study has 4 major conclusions. Firstly, Reading Speed (p = .24; a=.01) and Attitudes After Interaction ( p = .24; a= .01) have a significant positive correlation with Academic Performance, whereas Difficulties in Interaction has the largest significant negative correlation with Academic Performance (p = -.27;a= .01 ). The qualitative data showed that students experienced difficulties in graphs, tables, formulas and difficult terms (foreign words and scientific terms). Unclear and wordy explanations, the absence of a glossary, and non-detailed elaboration of graphs, tables, formulas, exercises and examples of the correct answers were major sources of difficulties that students mentioned. Secondly,there were significant differences between persisters and non-persisters in terms of Understanding of Directions, Reading Speed, Previous Academic Performance, and Time that Student Spent at work and on the way to/from work. Thirdly, Time on Interaction and Attitudes After Interaction had to be removed from the regression equation so that the combination of the remaining 10 factors could significantly explain the variation of Academic Performance (F = 2.11; R2= .24; R2 sig.= .0427). Finally, the combination of the 12 factors could predict correctly up to 65.22% of 3 groups of academic performance (high, medium, and low) and could predict correctly up to 64.10% of persistence.

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