The relationship between persistence, academic engagement and academic achievement among post graduate students of OUM By: Assoc. Prof. Dr. Nagarajah Lee Assoc. Prof. Dr. Chung Han Teck Prof. Dr. Rahmah Hashim. Assoc. Prof. Dr Lim Tick Meng

Overview Background Purpose Research Design, Population & Sample Instrument Findings Conclusion & Recommendation

Background Educational institutions have traditionally used academic variables such as grade point average (GPA), college admissions tests, and coursework grades (Adelman, 1999; Kern, Fagley, & Miller, 1998; Robbins et al., 2003; Tinto, 1997) to identify at-risk students. However evidence from the literature indicates that non-academic factors often have an even greater impact on academic performance. Among the variables that are found to have significant association with students’ performance are institutional and degree commitment, academic and social integration, support services satisfaction, finances, social support, and personality and psychological adjustment (Milem & Berger, 1997; Pascarella, 1985; Stage & Rushin, 1993; Tinto, 1993).

Background - cont Several studies have highlighted the significant role of affective factors on learning (e.g., Mathewson, 1994; Wigfield, 1997), particularly student engagement. Student engagement has been popularly used as an indicator of successful classroom instruction and predictor of students’ academic success (Bonia et.al 1997). Student engagement also refers to as "student's willingness, need, desire and compulsion to participate in, and be successful in, the learning process promoting higher level thinking for enduring understanding.” Willms, J.D (2003).

Students’ engagement is viewed as motivated behavior which can be seen from the kinds of cognitive strategies students choose to use, and by their willingness to persist with difficult tasks by regulating their own learning behavior. Persistence on the other hand is defined as adults staying in programs for as long as they can, engaging in self-directed learning, temporarily leaving the program, and returning to a program as soon as the demands of their lives allow (Beder, 1991). Persistence in this sense refers to the tendency or willingness of an adult learners to stay on a programme they have enrolled in. Both engagement and persistence are psychological factors that can be used as indicators to gauge students’ seriousness in their studies. It is therefore postulated both the factors can be reliable predictors of academic performance.

Due to the paucity of research that examines both student engagement and student persistence in studies simultaneously in relation to academic performance, this study proposes to incorporate both the factors in a statistical model to determine their validity as predictors of students’ academic performance Purpose

Conceptual Framework Student Engagement in Academic Activities Student Persistence in Studies Academic Performance

Research Design, Population & Sample This is a cross sectional survey using self- administered questionnaire. The population for this study comprises all postgraduate students of OUM enrolled in Masters degree programs and were actively taking service during the September 2010 semester. The sample consists of 390 randomly selected students from 4 masters programs (MBA, MEd, MHRM, MM and Nursing) from 13 learning centers.

INSTRUMENT Student Persistent Scale a)Academic Integration b)Service Satisfaction c)Degree Commitment d)Academic Conscientiousness e)Institutional Commitment Student Engagement Scale a)Classroom Behaviour b)Cognitive Emphasis c)Academic Contribution Respondents are required to rate their perceptions on a five-point Likert scale

Student Engagement Scale CMIN/df = ModeldfGFIAGFIRMSEANFICFIPNFIPGFI 3-factor oblique factor orthogonal One factor Goodness of Fit

Student Persistent Scale CMIN/df = ModeldfGFIAGFIRMSEANFICFIPNFIPGFI 5-factor oblique factor orthogonal One factor

Reliability ConstructsCronbach Alpha Coefficient Student Engagement Scale 1. Classroom Behaviour Cognitive Emphasis Academic Contribution Student Persistence Scale 1. Academic Integration Service Satisfaction Degree Commitment Academic Conscientiousness Institutional Commitment 0.840

VariableFrequencyPercentage (%)VariableFrequencyPercentage (%) GenderEthnicity Female and above Male to Total to ProgramBelow MBA Missing Med Total MHRM 51.3 Learning Center MM Ipoh Greenhill Nursing Johor Bharu Total Kedah SEMESTERKelantan Kuala Lumpur Kuching Melaka Miri NS Pahang Penang Sabah Missing Terengganu Total Total Ethnicity Malay Chinese Indian Others Total Respondents’ Demography

Respondent Demography vs CGPA CGPA Category p - value (Chi-Square) Less than and above Gender Female 21 (16.9%)103 (83.1%) Male 35 (17.2%)168 (82.8%) Ethnicity Malay 31 (20.1%)123 (79.9%) Chinese 12 (12.2%)87 (87.9%) Indian 6 (10.3%)52 (89.7%) Others 7 (38.9%)11 (61.1%) Age 30 and below 14 (25.5%)41 (74.5%) to (20.0%)104 (80.0%) 41 to (12.9%)74 (87.1%) Above 50 5 (9.3%)49 (90.7%) Gender, Ethnicity, and Age are not significantly associated with students academic performance. A single predictive model can be used to represent the post graduate student population * P <.05

Student Engagement vs CGPA Variable and Construct CGPANMeanSD p-value (Mann-Whitney ) Classroom Behavior Less than and above Cognitive Emphasis Less than and above Perceived Academic contribution Less than and above Engagement Less than and above Classroom behaviour and cognitive emphasis are significantly related to students academic performance. Students’ perceived academic contribution is not associated with their academic performance. * P <.05

Student Persistence vs CGPA Variable and Construct CGPANMean Std. Deviation p-value Academic Integration Less than and above Institutional Commitment Less than and above Service satisfaction Less than and above Academic Conscientiousness Less than and above Degree Commitment Less than and above Persistence Less than and above Academic Integration, Service Satisfaction, Degree Commitment, Academic Conscientiousness are significantly associated with students’ academic performance. There is no significant association between Institutional Commitment students’ academic performance. * P <.05

The Logistic Regression Model BS.E.WalddfSig.Exp(B) Step 1 a Behavior Cognitive Academic Academic Integration Institutional Commitment Service satisfaction Academic Conscientiousness Degree Commitment Constant a. Variable(s) entered on step 1: Behavior, Cognitive, Academic, Academic Integration, Institutional Commitment, Service Satisfaction, Academic Conscientiousness, Degree Commitment.

Model Fit Hosmer and Lemeshow Test StepChi-squaredfSig Model Summary Step-2 Log likelihood Cox & Snell R Square Nagelkerke R Square a a. Estimation terminated at iteration number 5 because parameter estimates changed by less than.001.

Model Sensitivity & Specificity Area Under the Curve Test Result Variable(s):Predicted probability Area Std. Error a Asymptotic Sig. b Asymptotic 95% Confidence Interval Lower Bound Upper Bound a. Under the nonparametric assumption b. Null hypothesis: true area = 0.5 Classification Table a Observed Predicted CGPA Category Percentag e Correct Less than and above Step 1 CGPA Category Less than and above Overall Percentage a. The cut value is.500 Sensitivity [ability to predict event correctly] = % Specificity [ability to predict non event correctly] = 91.04%

Logistic Model Explaining the relationships CGPA 3.00 and above = (Behavior) (Cognitive) ( Academic Integration) (Institutional Commitment) (Academic Conscientiousness) (Degree Commitment) Based on this equation, the probability for a student to get a CGPA of 3.00 and above is: P (E) =

Sample computation of probability VariableCoefficientStudents Rating Constant Behaviour Cogintive Academic Integration Ins. Commitment Academic Conscientiousness Degree Commitment Probability of getting CGPA 3.00 and above is0.760

Conclusion & Recommendations The findings of this study suggest that student engagement and persistence can be used as predictors of students academic performance. Using student engagement and persistence, a process measure, as predictors of academic achievement would enable the academic institutions to identify ‘at risk’ students much earlier compared to using CGPA, which is a product measure. Student engagement and persistence should ideally be used in conjunction with CGPA to identify ‘at risk’ students. This would enable academic institutions to formulate more effective intervention strategies to reduce attrition rate.

Thank You Questions and comments are welcome