The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016

Identification of Early Predictors of Adult Learners’
Academic Performance in Higher Education

Sylvia Chong Nguik Yin16	
  The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016

Abstract

Universities are inundated with detailed applicant and enrolment data from a variety of sources.
However, for these data to be useful there is a need to convert them into strategic knowledge
and information for decision-making processes. This study uses predictive modelling to
identify at-risk adult learners in their first semester at SIM University, a Singapore Universitythat caters mainly to adult learners. Fourteen variables from the enrolment database wereconsidered as possible factors for the predictive model. To classify the at-risk students, various
algorithms were used such as a neural network and classification tree. The performances of thedifferent models were compared for sensitivity, specificity and accuracy indices. The model
chosen is a classification tree model that may be used to inform policy. The implications ofthese results for identification of individuals in need of early intervention are discussed.
Keywords: predictive modelling; adult learners; higher education.

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  The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  Introduction
The ease of data collection and advances in information technologies, such as storage
capability, processing power and access speed, has enabled educational institutions toaccumulate vast amounts of data. Universities and their enrolment offices are inundated withdetailed applicant and enrolment data from a variety of sources, such as student demographics,
professional experience and academic background. However, for these data to be useful there
is a need to convert them into strategic knowledge and information for decision-making
processes. Over the past decade, data mining has gained increasing attention in academia togenerate data driven evidence (Koh and Chong, 2014). Data mining approaches can discoverhidden relationships and patterns. These relationships and patterns can, in turn, be developed
into models to predict students’ performance and behaviour. The predictive models can develop
knowledge and insights, on which informed and strategic decisions can be made.
The purpose of this study is to develop predictive models to identify early predictors ofacademic performance of adult learners who are enrolled in the part-time undergraduate
programs at SIM University, Singapore. SIM University is Singapore's only privately-funded
university dedicated to working adults. The University has provided pathways for many topursue lifelong learning and higher education while balancing career, family and social
responsibilities (SIM University, 2014).The research scope was developed in the context of the
SIM University’s enrolment process. The research is timely and significant because of thegrowing number of adult learners who are returning to higher education (Macfadgen, 2007).
his paper focuses on the factors that predict adult learners who may be academically at-risk andproposes incorporating into the enrolment process a predictive model to identify potential atrisk students.
Context of Study
The profile of students in higher education is changing (Chong, Loh and Babu, 2015). There isan increasing number of non-traditional students – these are students who are not in the groupof 18-22 year-old full-time undergraduates (Wyatt, 2011; Macfadgen, 2007). There are 13,369
adult students currently enrolled in SIM University (SIM University, 2014). This is significant,as more and more adults who have been out of school for some years are turning to higher
education institutions to start, continue or complete undergraduate degrees. In August 2012,
the Singapore government declared support for the continuing higher education sector by
expanding and diversifying the pathways in higher education (MOE, 2012). The restructuringof higher education pathways and institutions ensures that Singapore develops a morecompetitive workforce. The Singapore government, in a bid to encourage and support lifelong
learning and continuing education, has made available a range of financial support instruments
such as government subsidised bursaries and tuition loan schemes for adult learners to take up
part-time undergraduate programs in SIM University (MOE, 2012). SIM University must
modify and target their enrolment and admission strategies to better serve this growing
population of adult learners. It is important for SIM University to identify and profile students
who will eventually succeed, as well as applicants who will struggle or are inappropriate foradmission.
With growing participation adult learners in higher education, SIM University must sift throughan increasing number of applications. Making informed enrolment decisions will require
accurate data and analysis for evidenced-based insights and knowledge discovery.
Incorporating into the enrolment process predictive models to identify potential at-risk students
or student success is highly advantageous. A combination of an explicit knowledge base
together with sophisticated analytical approaches and clear domain information can uncover
	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	  patterns, associations and/or relationships to support enrolment management. By analysing
enrolment data, it is possible to develop models that will be able to predict the potential of incoming students.
Review of Literature
The review of literature is organized in two parts. The first section includes an overview of thedata mining process and its use in higher education. The second section provides a review ofstudies on predictors of academic performance in higher education.
Use of Data Mining in Higher Education
Data mining has emerged in the wake of higher education’s ability to capture a rapidly growing
amount of data to “develop models for improving learning experiences and improving
institutional effectiveness” (Huebner, 2013). The data mining process is often initiated without
any preconceived outcomes; it adopts a data analysis methodology (Chong, Mak and Loh,2016) and is often interchangeable with the term Knowledge Discovery in Databases (KDD)(SPSS, 2009) with the aim of obtaining insightful and useful findings (Giudici, 2013). In its
basic form, the data mining process is the extraction of the knowledge within large databases.
The data mining process involves several phases among which are: data acquisition, feature
selection and extraction from database, model development and pattern recognition using data
mining techniques, model interpretation and knowledge generation. Data mining, used inhigher education, can strategically combine selected institutional data and statistical analysis
to generate information upon which students, educators, administrators and management can
improve practices. This highlights the importance of data mining as an approach to build
models by transforming raw data into usable knowledge and information (Giudici, 2013).
Chang’s (2009) study used data mining techniques to develop a model to predict the academic
performance of university applicants. The predictive model was developed based on variables
taken from the university’s integrated admissions system. The integrated system included
databases on application, enrolment and student progress data. The study showed that theneural network and decision tree models developed were able to inform university recruitment
strategies, as well as support institutional research. Ramaswami and Bhaskaran (2010) used a
CHAID (Chi-square Automatic Interaction Detector) prediction model, based on aclassification tree, to identify a set of predictive variables and assess the impact of thesevariables on the academic performance of university students. A pilot experiment with 224
students from two different universities along with 35 variables was conducted. The model
showed a strong correlation between attributes such as location, school type, parents’
education, secondary school grades and the students’ performance at the universities. Kovacic(2010) developed prediction models of students’ success based on enrolment data withstatistical techniques such as CART (Classification and Regression Technique) and QUEST(Quick, Unbiased and Efficient Statistical Tree) classification tree methods. He concluded thatclassifying students based on pre-enrolment data helps to identify students who may be at risk,
and recommended orientation, advising and mentoring programs to support these students.
The literature also indicated that algorithmic or data mining approaches to develop predictivemodels could provide notable results vis-à-vis traditional statistical modelling approaches (Li,
Nsofor and Song, 2009; Bogard, James, Helbig & Huff, 2012). Vandamme, Meskens andSuperby (2007) used decision trees, neural networks and linear regression for the early
identification of three categories of first-year students: low-, medium- and high-risk students.
Some of the demographics and academic variables of these students were significantly related

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	  to academic performance. Such predictions are useful to identify and support students withappropriate interventions to improve their academic performance.
Predictors of Academic Performance
The antecedents to success in university prior to students’ matriculating are well established.
Evidence exists to show that pre-university academic performance has a significant impact on
subsequent academic performance in university. The relationship between pre-university
grades and university performance has been validated in studies (Iam-On and Boongoen, 2015;
Adelman, 2006). Adelman’s (2006) research on persistence points to the importance of bothpre-university (high school) performance and the rigor of the high school curriculum. Iam-On
and Boongoen (2015) affirmed the importance of pre-university grade-point average inpredicting success in university. However, the predictive ability of pre-university school grades
is different for different individuals and groups. Power, Robertson, and Baker (1987) showedthat the correlation between pre-university/high school grades and Grade Point Average (GPA)
at university is generally about 0.5. They also found that secondary school grades are not as
predictive for mature students’ performance as they are for school leavers’ performance.
According to Bhardwaj & Pal, (2011) personal, social, psychological and environmental
variables have an impact on students’ academic performance. Other variables such as living
location, medium of teaching, mother’s qualifications, and family annual income also
potentially affects student performance (Bharadwaj & Pal, 2011). Demographic variables thathave been found to be determinants of academic performance include age, gender, employment
responsibilities, and student workload (Palmer, Bexley and James, 2011).
In addition, pre-university factors that are commonly associated with individuals most at risk
include: low pre-university school grade-point average (Adelman, 2006), low SAT/ACTscores; minority status (Pascarella and Terenzini, 2005), low family education levels, and low
family income (Eagle and Tinto, 2008). Other variables that contribute include non-cognitive
factors such as motivation, aspirations (Eagle and Tinto, 2008), and tendencies toward social
and academic integration (Braxton and Hirschy, 2005; Eagle & Tinto, 2008). These noncognitive factors are also seen as predictors of academic performance. Investigating theinteraction of more traditional risk factors, such as demographics, with early engagement
indicators can lead to a richer understanding of the predictors of success for students.
Research Objectives
The key purpose of this study is to identify early predictors of academic performance during
the adult learners’ initial semester at university using a data mining approach. Through this,SIM University hopes to identify students or applicants who are academically at risk as earlyas possible. Decision trees are used to build these models so that appropriate enrolment andintervention strategies can be designed and implemented. Specifically the study aims to achieve
the following research objectives:•   Identify characteristics that are available at application and early engagement
variables of adult learners who are academically (GPA) at-risk in higher education•   Build models for early prediction of the academically at risk with the identified
application characteristics and early engagement variables•   Evaluate these models using cross-validation

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	  Research Methodology
Data Source
The target sample for this study was comprised of first-year students who started their parttime degree program at SIM University in January and July 2013. Data was extracted from an
in-house student information management system which collects and catalogues data fromnumerous sources within the admissions office as well as in other divisions of the university.
For the purpose of this study, a range of demographic and academic data was extracted for
2,392 students that fall within the target sample.
Data Understanding
In order to identify potentially useful and credible patterns in the data, several iterative steps
were taken in the development of the enrolment model. Students with missing data were
removed from the dataset because some data mining algorithms were not able to handle missing
data. To assist with data understanding, profiling was also conducted to determine theproportion of at-risk students in relation to the overall student participation rates.
Profile. Students studying in SIM University were relatively equally distributed in terms ofgender, with more than half of the sample being between 21 and 25 years of age (M = 26.5, SD= 4.98). 22.7% of the students were identified as at risk students based on their Pre-University
Cumulative Grade Point Average (CGPA) (See Table 1 for more details).
	
  

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Table 1. Profile of Sample Used for ModellingN%

Female

1147

48.0

Male

1245

52.0

21 to 25

1387

58.0

26 to 30

599

25.0

Above 31

406

17.0

At Risk

542

22.7

Not At Risk

1850

77.3

SASS

546

22.8

SBIZ

848

35.5

HDSS

291

12.2

SST

707

29.6

GenderAge

At Risk Status

SIM University School

Variables. Predictors for the study can be broadly categorized into the three following
groups. The description of the variables is presented in Table 2.
•   Demographic variables (gender, age, marital status, race, length of working experience]•   Pre-SIM University academic performance indicators (prior diploma school, diploma
CGPA, years since they last studied, field of diploma study, relevance of previous
diploma study to current degree, O-Levels English and O-levels Mathematics) and•   University variables (SIM University schools and Credit Units (CUs) registered).
Demographic variables. As a university dedicated to adult learners, SIM University’senrolment is typically characterized by a diverse student profile in terms of their race, marital
status, age and working experience. In view of this, demographic variables are of particularinterest as students at different life stages handle the demands of a university program
differently.
Pre-SIM University academic performance indicators. The concept of students’ innate
academic aptitude and its impact on their ability to cope with the demands of a university
	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  education has been discussed in literature (Pascoe, McClelland and McGaw, 1997). In view ofthis, proxy indicators like O-levels English and O-levels Mathematics, subjects that most
students offer at national examinations were collected to represent the students’ academic
competence. In the same token, diploma GPAs and the field of their diploma studies may also
serve as a good gauge of the student’s aptitude in respective programmes.
SIM University variables. SIM University’s programmes are offered by four schools thatcover a range of disciplines: School of Arts and Social Sciences (SASS), School of Business(SBIZ), School of Human Development and Social Services (HDSS), and School of Scienceand Technology (SST). As it is likely that programs offered by each school required andemphasised different domain knowledge and skills, it is insightful to identify and study
between-school differences on the students’ academic performance. Course workload isanother predictor of interest in this study. To capture course workload, the number of Credit
Units (CUs) that the students registered for at the start of the semester is used as a proxy.

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  Table 2. Variables used for data exploration and analysis
Variable Role Level
Role
Description/ TypeAt Risk Indicator

TargetDemographic Variables:
GenderPredictor Student gender (binary: male or female)

Age

Predictor Student age at intake (numeric)Marital Status

Predictor Student marital status (binary: Single or Married)

Race

Predictor Student race (nominal: Chinese, Malay, Indian or
Others)
Predictor Student length of working experience in months(numeric)

Work Experience

Student academic risk status (binary: at risk or
not at risk)

Predictor The institution that student obtained Diploma
Name of Diploma
Pre-SIM University academic performance indicatorsfrom (nominal: A, B, C, D, E)
Awarding Institution(DIP Institution)
Diploma CGPA
Years since study
Field of Diploma Study
Relevance of Diploma
Study
Mathematics “O” level
Grade
English “O” level Grade

Predictor Student diploma final CGPA attained (numeric:
0.0 to 4.0)
Predictor Number of years since student last studied(numeric)
Predictor The diploma area of study that student previously
graduated from (nominal: Engineering, Business,& student
Design,diploma
Health Science,
AppliedPredictor Media
Whetherfield of studyisSciences,
Built
Environment,
Information
relevant to the degree he/she is pursuing (binary:Technologies,
relevant or not Humanities
relevant) and Maritime Studies)
Predictor Student previous Math grades (ordinal: 1 to 9)
Predictor Student previous English grades (ordinal: 1 to 9)

SIM University Variables
SIM University School
CUs Registered

Predictor The school which the student is currently
enrolled in (nominal: SASS, SBIZ, HDSS, SST)
Predictor Number of credit units student registered for thatsemester (numeric)Modelling
A binary target variable ‘at risk’ was also constructed where students with a CGPA score of
2.3 and below is flagged as at risk while those with a CGPA score of above 2.3 is flagged as
not at risk. The threshold CGPA cut-off of 2.3 was used to be consistent with SIM University’s
practice of offering academic counselling to students with a CGPA score of 2.3 and below.

	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  After data preparation, a data driven approach was used to select statistically significant
predictors. Using IBM SPSS Modeler 14.1, a list of significant predictors was identified using
Model Feature Selection node, Neural Networks, CHAID, C5.0 and CRT based on theirstatistical significance (p-value <.05). As each algorithm has its own computation methodology
strengths, comparing the list of predictors chosen by different algorithms offered a balanced
and insightful approach in short listing variables that are consistently important for subsequentmodelling. This controlled for variable selection bias. The list of short listed variables was thenevaluated based on inputs from the literature as well as by subject matter experts who havecontextual knowledge of the workings of UniSIM and the Singapore education landscape.
In model building, the CHAID decision tree was chosen as the baseline decision tree among
the other decision trees that were developed via different algorithms on the full dataset (N =2,392). The selection of the baseline model was based on an evaluation of a basket of criterion
which measured the models’ specificity, sensitivity, accuracy, and G-mean (Kubat, Holte &Matwin 1997). Collectively, the different criteria represented the models’ ability to correctlyclassify at risk students, correctly classify not at risk students, and measure the degree ofcloseness of predicted values to actual values and measure the trade-off between specificity
and sensitivity respectively.
Subsequent to this, the team attempted to build a contextualised decision tree for UniSIM which
could better the predictive performance of the baseline decision tree. In this phase, greater
emphasis was placed on literature and domain knowledge whereby different predictors wereused as the first tree splitting criterion. All these predictors were selected based on theirstatistical significance (p-value < .05) as well as their influence on the students’ performanceas observed from domain knowledge. After the alternative CHAID trees were grown, a 10-foldcross validation was applied to ascertain their stability and to prevent over-fitting. In instancesof significant deviation in performance criterion the outliers were removed, a model wasreconstructed and cross-validated using the same process. Lastly, performance criteria of all
the alternative CHAID models were compared and evaluated. The final CHAID model which
presented an optimal balance in its accuracy, stability in predictive performance andexplanatory power was chosen.

	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    Results and Discussion
Findings from Data Understanding 	
  As part of data understanding, a cross-tabulation was done for each variable listed in Table 2
to understand the proportion of at-risk students compared to the student participation rates. The
results of selected variables in Table 3 revealed some interesting patterns. 	
  	
  Table 3. Summary results of selected variables of the at-risk model
1st split
criterion:
DIP
Institution

Institution A

Institution B

Institution C

Institution D

Institution E

Probabilityof Sem1_Outcome= at-risk

0.357

0.213

0.243

0.187

0.167

2nd split
criterion:
DIP
CGPA

≤3.08

>3.08

≤2.09

2.09 to
3.30

>3.30

≤2.91

>2.91

≤2.09

>2.09

≤1.86

1.86
to
3.08

>3.08

Probabilityof Sem1_Outcome= at-risk

0.395

0.186

0.320

0.205

0.078

0.298

0.148

0.377

0.135

0.292

0.179

0

Yrs
since
study
end

‘O’
level
EnglishNo
further
split

Sch

Sch

3rd split
criterion:
Varies
4th split
criterion:
VariesNo further
split

No further split

‘O’
level
Maths

No
further
split

Sch
No
furthersplit

No further split
‘O’
level
Maths

The pre-UniSIM academic performance indicators such as Diploma CGPA, Mathematics ‘O’level grades and English ‘O’ level grades, if the students had a lower poly GPA score (< 2.00)
or weak ‘O’ level English and Maths grade (C6 or less), a higher percentage of the students
were classified as at risk. It seems that the diploma awarding institution may have had someinfluence on the students’ academic performance as a substantial percentage of graduates from
Institution C are classified at risk (21.2%) compared to their participation rate (13.5%).
Evaluation and Validation of Model
Based on the confusion matrices presented in Table 4, the three alternative CHAID models
offer a more balanced predictive performance than the baseline reference model given theirhigher G-Mean scores (defined as a Geometric mean of Specificity and Sensitivity (Kubat,
Holte, & Matwin, 1997). Out of the 3 models, the DIP Institution model was selected as it
offers comparable specificity and sensitivity indices with no significant trade-off in otherevaluation criteria. In this instance, it is important that the model has a good hit rate (evaluatedholistically based on specificity and G-Mean indices) since the practical cost of
	
  

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The  IAFOR  Journal  of  Education  Volume  4  –  Issue  2  –  Summer  2016	    misclassification would mean that actual at-risk students would not be able to benefit from
subsequent intervention strategies or support.
Table 4. Comparison of evaluation criteria on CHAID models with different factors as tree
splitting criteria

Specificity
Sensitivity
Accuracy
G-MeanError rate

Reference Model

DIP Institution

Diploma CGPAUniSIM Schools

40.8%
84.8%
74.8%
58.8%
25.2%

50.9%
76.8%
70.9%
62.5%
29.1%

50.4%
75.4%
69.7%
61.6%
30.3%

48.7%
78.4%
71.7%
61.8%
28.3%

The chosen DIP Institution model was then tested for its stability and replicability using the
10-Folds Cross Validation method. The cross validation result that is presented at Table 5
suggests a reasonably stable model and consistent predictive performance.
Table 5. 10-folds Cross Validation Performance of the chosen DIP Institution CHAID modelSensitivity
Ability to correctly identify actual cases (true positives)
Specificity
Ability to correctly identify negative cases (true negatives)
Accuracy
Closeness of its prediction to the actual values (true positives& negatives)
Error Rate
Proportion of incorrect predictions (true negative &positives)
G-MeanGeometric mean of sensitivity and specificity

Chosen DIP
Institution
CHAID model

10 Folds Cross
Validation

76.8%

77.3%

50.9%

48.1%

70.9%

70.8%

29.1%

29.2%

62.5%

60.1%

Upon examination of the final CHAID decision tree (see Figure 1), we find that the DIP
Institution (x2 = 44.66, p-value < .05) that the students graduated from is significant. It is also
observed that the CHAID decision tree divides into three branches with a few DIP Institutionsgrouped together (for example, DIP Institutions A, B and D are grouped at 1 split, while Dip
Institution E remains by itself). This could perhaps be attributed to a lack of comparable grading
criteria adopted by different DIP Institutions. This is an indication that the quality of their prioracademic preparation is an important influencing factor on the adult learners’ ability to cope inthe degree program in addition to their innate academic potential.

	
  

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The  IAFOR  Journal  of  Education    Figure 1. Final CHAID Model
	
  

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Volume  4  –  Issue  2  –  Summer  2016	
  The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  Implications and Application of Findings
In the CHAID model (see Figure 1), pre-SIM University academic performance variables: the
Pre-University institution that the students graduated from, students’ CGPA score and ‘O’ level
English and Mathematics grades emerged as significant predictors of the adult learner’s
academic performance for the first semester. The finding that the pre-university institution thatthe students graduated from is a key predictor indicates that there is a wide variation in thestandards of performance among the different diploma institutions. Singapore’s education
system is essentially centralized and standardized (Lo, 2014). This variation in academic
performance standards among feeder institutions is an issue of concern for the University’senrolment office.
The quality and strength of the students’ academic foundation prior to entering University
impacts how they cope with the demands of a university program. The finding that preuniversity diploma CGPA is a significant predictor of academic outcomes is consistent with
Geiser and Santelices’ (2007) study that concluded that high school GPA is consistently astrong predictor of four-year college academic outcomes.
The importance of a strong pre-university academic foundation is consistent with views thathigh school English and mathematics proficiencies are critical parts of undergraduate
preparation for success. Research also demonstrates that language proficiency is correlatedwith academic success (Ellis, Chong & Choy, 2013; Gottlieb, 2006). Goldinch and Hughes(2007) investigated the relationships between students' confidence in their generic skills on
entry to university, their learning styles and their academic performance in the first year. Theirstudy highlighted a link between students’ confidence with language and numeracy
proficiencies.
The model developed in this study can be of assistance to university enrolment management inmany ways. An awareness of how potential students may perform academically could lead toa more targeted marketing campaign. Promotional materials about academics, mentoring andstudent support resources can raise applicants’ awareness of how these services can aid in adultlearner transition to university. With the identification of significant factors that may affect thestudents’ initial academic performance, universities can provide timely interventions throughearly identification and monitoring of possible at-risk students. A multi-pronged support
structure may be more efficacious in assisting these students to remain in their degree program.
Concrete steps, such as academic counseling may be offered to targeted students to maximise
their learning and overall university experience. In this way, resources can be more effectively
and efficiently targeted towards a comprehensive support for these students.
Future Directions
Although this study is limited in that it is based on SIM University’s 2013 enrolment dataset
the proposed model may serve as a baseline for future research. Another limitation of thepresent study is that the academically at risk status (operationalized as CGPA) was assessedfor only a single academic year. There are three potential future research directions. Firstly,
more variables could be included to improve the enrolment models as well as to develop otherrelevant models with reduced misclassification of students’ academic performance. Other
decision tree models and ensembles of models may also be explored as educational institutions
exploit data mining for effective decision making, efficient operations and to improve teaching
and learning (Koh and Chong, 2014). Secondly, there is a need to follow various groups ofstudents, students who are at risk, transferees, withdrawals, as well as students who are high
	
  

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The  IAFOR  Journal  of  Education    Volume  4  –  Issue  2  –  Summer  2016	
  performers. Including a time line in the analysis to follow these groups of students insubsequent semesters and tracking their study outcome would help to model their learning
behaviour and patterns. Thirdly, aside from enhancing the accuracy of prediction, one of thedirections for future research could be focused on using the data collected to identify anddevelop the support systems for teaching and learning.
Acknowledgement
This paper is part of an institutional research made possible through a grant (code:
RF13LSC01) provided by SIM University.

	
  

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