'n Model van faktore wat die sukses van onderrigleer van tegnologie-gebaseerde modules beïnvloed

Abstract

Dit wil voorkom asof daar groepe studente is wat meer probleme ondervind met die bemeestering van tegnologiegebaseerde modules as ander modules. Bestaande navorsing dui op spesifieke faktore wat sterk figureer in die sukses van onderrigleer van tegnologiegebaseerde onderwerpe. In bestaande navorsing word hierdie faktore egter geïsoleerd beskou en nie in alle gevalle empiries beoordeel nie. Die doel van die studie waarop hierdie artikel gebaseer is, was om ’n model van faktore wat die sukses van onderrigleer van tegnologiegebaseerde modules beïnvloed te ontwikkel. Daar is gebruik gemaak van gemengde navorsingsmetodes. ’n Literatuurstudie is onderneem om faktore wat moontlik ’n invloed op die sukses van onderrigleer van tegnologiegebaseerde modules het, te identifiseer. Interpretatiewe navorsing, deur middel van onderhoude en vraelyste, is hierna onderneem om die faktore wat uit die literatuur geïdentifiseer is, te verifieer en die model aan te vul. Nadat vraelyste deur studente voltooi is, is statistiese verwerking gedoen om die bydraes van die verskillende faktore in die model te toets. Die eindresultaat is ’n model van faktore wat die sukses van onderrigleer van tegnologiegebaseerde modules (soos toegepas op ’n rekenaarvaardigheidsmodule) beïnvloed. Die student se prestasie in die finale skooleksamen word aangedui as die faktor wat die grootste bydrae lewer. Naas skoolprestasie is voorkennis die volgende belangrikste faktor. Dit het verder geblyk dat manlike studente beter as vroulike studente in die rekenaarvaardigheidseksamen presteer. Studente wat wiskunde, natuurwetenskap, rekeningkunde of rekenaarstudie as hulle gunstelingvak gekies het, het beter in die eksamen presteer as studente wat tale of leervakke uitgesonder het. Studente wat meen dat hulle eendag rekenaarvaardigheid in hulle werk gaan gebruik, het beter as ander studente presteer. Studente met hoë rekenaar-angs het swakker in die eksamen presteer as dié met min rekenaar-angs. Laastens het selfoonbesit ook ’n beduidende invloed op die uitslag van die rekenaarvaardigheidseksamen gehad.

A model of the factors that influence the success of teaching and learning of technology-based subjects

There appear to be groups of students who experience more problems with the mastering of technology-based subjects (where technology is used, but is not the main subject of the study) than other (non-technology-based) subjects. A study was therefore conducted to identify factors that influence the success of education in technology-based subjects. Existing research points to specific factors that figure strongly in the success of the learning of technology-based subjects. In existing research these factors are, however, viewed in isolation and are not always evaluated empirically. In the research on which this article is based the influence of various factors is investigated, compared and empirically evaluated together. The objective of the study was to develop a model of the factors that influence learning success in technology-based subjects.

In this research project methods from different research paradigms were used in different phases of the project. Since there was no clarity about the factors to be investigated, the interpretative methods were initially used to identify factors by means of interviews and open questions. These results were analysed by means of coding. After a model of possible factors was compiled in this way, methods from the positivistic paradigm were applied. Questionnaires were compiled to test the model and the results of these questionnaires were statistically analysed.

The study was undertaken at the Potchefstroom campus of North-West University The purpose of the literature study was to investigate existing publications on factors influencing a student’s success in mastering technology-based subjects. The most important factors highlighted in the literature are gender, language, race or culture, prior knowledge or entry level, socio-economic status, learning styles, computer anxiety, the student’s confidence in his or her own abilities, and the student’s vision of the future.

After the literature study had been concluded, interviews were conducted. The results of the interviews were used to expand the model and also to identify specific questions to be included in the questionnaires. While processing the interview results, various important factors became evident, namely prior knowledge, planning/time management, computer anxiety, secondary school level and language.

The purpose of these questionnaires was to identify additional factors that may influence learning of technology-based subjects by means of open questions. Furthermore, the relevance of factors already identified was investigated. The open questions in the questionnaires were processed interpretatively. From the answers to the open questions it was deduced that the interest of students may have an influence on the success of learning. Another factor mentioned was the student’s intellectual abilities. Factors already included in the model, but of which the importance was once again emphasised here, were self-confidence, prior knowledge, time management and language.

After data had been collected by means of questionnaires, biographical data and examination marks, a large number of variables were available for use to test the relationships in the model. First of all, factor analysis was done to determine which variables measured the same factor, in order to reduce the number of variables.

The factors not included in the factor analysis (their nature not being suitable for factor analysis) were added. These are gender, language, race, secondary school level, school performance and favourite subject. This model was then used, together with multiple linear regressions, to test which of the different relationships in the model were significant.