Factors Affecting Teacher Student Competencies According to the ASEAN UNIVERSITY NETWORK QUALITY ASSURANCE (AUN QA) Guidelines

Abstract

Objectives of the study were 1) to study the factor levels affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA), 2) to study the level of teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA), and 3) to study factors affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA).

 The target group in the study was  121 informants consisting of an administrator and 10 lecturers from each faculty of education  of 11 Faculty of Education in the North by purposive selection.

The research instruments employed were a 3-part questionnaire with IOC value between0.67-1.00. Reliability in factors affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA) is 0.86 and teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA) is 0.86. Data were analyzed using computer software package. The statistics used in the study included frequency, percentage, mean, standard deviation, and Multiple Regression statistics.

Findings were as follows: 1) the factor levels affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA)  is high ( = 4.27,S.D.= 0.61). 2) The level of teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA) is high ( = 4.31,S.D.= 0.59), and 3) factors affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA) consisted of  two factors, overall factor (X_t) and expected learning outcome process (X_a) is predictive variable affecting teacher student’s competencies based on ASEAN UNIVERSITY NETWORK  QUALITY ASSURANCE (AUN QA) statistically significant at the .01 level with a multiple correlation coefficient (R) of 0.733, equaling 0.733 predictive correlation (R²), equaling 0.537 or 53.70% with predictive power and regression coefficient (Adj.R²), equaling 0.525 or 52.50%, which can be written as a predictive equation as follows:  ,

Predictive equation in raw score

Ŷ = 1.103 + .422 (X_t) + .321 (X_a)

Predictive equation as standard score

Ẑ = .439 (X_t) + .325 (X_a)