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Social and Behavioral Sciences
Procedia - Social and Behavioral Sciences 90 (2013) 549 - 557
6thInternational Conference on University Learning and Teaching (InCULT 2012)
The Validity of ASSIST as a Measurement of Learning Approach
among MDAB Students
Nur Fadhlina Zainal Abedina, Zuraida Jaafarb, Sakinah Husainc, Rosnani Abdullahd*
acFaculty of Business Management, Universiti Teknologi MARA, 72000, Kuala Pilah, Negeri Sembilan dFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 72000, Kuala Pilah, Negeri Sembilan
Abstract
Students have their own style to receive and respond to learning process. Recognition of student's learning approach is important and the focus should be on education because it is a key factor in the formation of an individual. There are many instruments have been developed to measure students' approaches of learning includingApproaches and Study Skills Inventory for Students (ASSIST). Thus, the aim of this study is to explore an instrument and to test the predictive validity of (ASSIST) on current and past MDAB students. The questionnaires were distributed the students in UiTM Negeri Sembilan, comprising a total of 112 samples. ASSIST is atype of questionnaire that has been widely used in studies of learning approaches. Three groups were identified in this questionnaire: Deep, Strategic and Surface approaches. One section out of four sections in the questionnaire will be used- Approaches to Study, which consists of 52 items.The researchers will examine the instrument through Path Diagram Analysis by using SPSS and Analysis of Moment Structures (AMOS) programme.The findings of this study show that ASSIST is an appropriate instrument and proven valid through the scores obtained in the main scales and subscales. Thus, ASSIST can be used as a valuable instrument to access approaches to learning of MDAB students in Negeri Sembilan. We also find that there are significant and positive correlations between deep, surface and strategic approaches.
©2013 TheAuthors.PublishedbyElsevier Ltd.
Selectionand/orpeer-reviewunder responsibilityoftheFaculty ofEducation,University TechnologyMARA,Malaysia. Keywords: Approaches to learning,; ASSIST; path diagram analysis
1. Introduction
Learning can be defined as the acquisition or pattern that is preferred when processing new information or experiences. When such information is obtained, learners will interpret or interact with the
* Corresponding author. Tel.: +06-4832194. E-mail address :nurfadhlma@ns.mtm. edu.my
1877-0428 © 2013 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of the Faculty of Education, University Technology MARA, Malaysia. doi:10.1016/j.sbspro.2013.07.125
information with their own style or approach. The ways they evaluate the information are different from one to another as each person has his or her own learning approach. Generally, learning approach is the way an individual interacts with the information obtained. The individual processes and analyses the information and it becomes knowledge.
The differences of approaches among students include the aspect of individual thoughts, reactions, interests, preferences, achievements and understanding. Thus, these students have their own style to receive and respond to learning process. Recognition of student's learning approach is important and the focus should be on education because it is a key factor in the formation of an individual.
Many instruments have been developed to measure students' approaches. For example, Biggs (1987) designed the Study Process Questionnaire (SPQ) and Weinstein Schulte Palmer (1987) designed the Learning & Study Strategies Inventory (LASSI). Meanwhile, Entwistle & Ramsden (1982) developed the Approach to Studying Inventory (ASI), which had been widely used to assess students' learning in higher level education. However, ASI has limitations in reliability & validity. The questionnaire was then modified in Approach Study Skill for Student (ASSIST), which was developed by Martin & Saljo (1976) and Tait, Entwistle & McCune (1998).
Three approaches were identified in ASSIT: Deep, Surface and Strategic learning approaches. The idea of Deep, Surface and Strategic approaches to learning has been in existence for many decades and has been a foundation stone in many researches, formulation of theories and practices, mostly in institutions of higher education. Deep Approach learning is an exciting and gratifying challenge in learning. In this approach students focus on significant issues in a particular topic, and relate their previous knowledge to new knowledge. Basically they read wisely- relating the ideas with another subject; examining logic and arguments carefully and critically; then, checking evidence and relating it to conclusion. Those students who adopt this kind of learning have more systematic organisation of ideas, and are able to recall and apply easily the ideas or knowledge. Learners who adopt Deep Approach intend to understand information better and usually correlate with more positive academic performance. Abdullah (2004) found that there is relationship between academic performance and learning style. He concluded that students who adopt Deep Approach have better academic performance compared to those who used other approaches.
In contrast to Deep Approach, Surface Approach is well-known for its rote learning process, focusing only on key words and covering many facts by memorisation. Learners of this approach tend to rely heavily on word-forword notes; jotted down from what is said in lectures. They memorise the notes without making any effort to link different parts of the information; and they try to reproduce these notes in essays, tests and examinations. Apart from that, they tend to focus on and memorise only specific details rather than to understand all parts of information. Their intention is only to complete the task or learning requirements. Basically, those students who love to adopt this kind of approach are afraid of failure. They feel under pressure and stressed up. They always worry about their works and normally have poor academic performance.
There is another approach of learning known as 'achieving' or Strategic Approach. This approach employs both Deep and Surface Approach. Learners organise their time and working space efficiently and choose appropriate readings or tasks that they think will enable them to get the best marks. This approach is very flexible. If time is short or if the assessment requires memorisations, the learners will adopt Surface Approach as the appropriate tool. When more time is available, the learners will use the Deep Approach and develop deeper understanding. Students who adopt this Strategic Approach are fully alert to any assessment requirements and criteria needed. Learners of this approach try to find out what a teacher wants. They then prepare and try to provide all the information or answers required. It is likely that these students are motivated by 'fear of failure'. Those students who apply Strategic Approach will attempt to maximise their performance and grades. Figure 1 below illustrates the components of effective study in ASSIST.
Most studies relate learning approaches to performance in education. Recent studies by Ryan & Irwin et al (2004), Huy. P. Phan (2006) and Tarabashkina.L & Lietz.P (2011) examined how personal value influenced students' approaches to learning, which in turn, were related to their achievement. They found that students who displayed more characteristics of Surface Learning Approach had lower academic performance. This was
supported by earlier studies by Kember.D & Jamieson.Q (1995) who concluded the following observation. Students using Surface Approach took a long time to study, had high attendance in class but achieved poor academic performance. Those students who used Deep Approach were not necessarily good in their grades unless accompanied by sufficient work (Strategic Approach).
Fig. 1. Conceptual mapping of components of effective studying from ASSIST (Source: Entwistle& McCune, 2000)
Some researchers argue that students' learning approaches are also influenced by teaching approach of lecturers. Studies by Rohazal Abdullah (2004), Kevin Warbuton (2003) and previous studies of Trigwell. Ketal (1999) examined the relationship between teaching approach and learning approach. They found that, when teachers/lecturers focused on what they did in teaching rather than students' doing and learning, students were more likely to adopt surface learning. Their conclusion is this: when a teacher encourages self-direct learning, discusses and interacts with students about the problems encountered, the teacher stimulates debates and uses a lot of time to question students in order to develop conversation with students in lectures. As a result, students are less likely to adopt Surface Approach. They are indeed encouraged to use Deep and Strategic Approach.
This kind of teaching approach ensures that students understand what they learn and not merely taking notes without thinking. Development of deep understanding depends on the characteristics of learning approaches adopted by students (Chris Cope, 2003). Strongly supported by studies of Fun Lan Yang (2010) and GabriellMckee & Aileen Patterson et al (2009), Deep or Strategic Approach to learning are more desirable than
Surface ones. In order to improve the quality of student learning, lecturers must raise the awareness of students concerning their learning approaches. In addition, they need also to encourage the students with teaching assessment practices, which will assist them to understand lessons well. Marion T. Ryan & Jane A. Irwin et al (2004) cautioned about the impact of high workload, which may compel students to employ Surface Approach of learning.
Sometimes, students' learning approaches could be changed due to the type of assessment given. Students are more likely to employ Surface Approach but with no surface motive, when they are preparing for examination essays. They switch to Deep or Strategic Approach and motive when they are writing assignment essays (Karen Scouller & Elaine Chapman, 1999). Hesham F. Gadelrab (2011) recently carried out a research among students in an international university in Egypt pertaining to assessment procedures applied in Egyptian higher education system. He found that the assessments often reward those who are concerned with both the academic content and the course grades. This group of students keep in mind how to organise answers in a way that impresses the marker, and they have also memorised materials to meet the requirement of examinations. The assessment system provides legitimate reasons for students to expect such approaches.
Apart from the above, students' preferences for learning approaches are likely to be different from one another, varying in accordance to background and culture. Gregory Boland & Satoshi Sugahara et al (2011) conducted a study recently to explore the role of cultural differences on students' learning styles in three different nations- Japan, Australia and Belgium. They found that there was a relationship between cultural factors and learning style preferences. The degree of individualism had significant effects in driving students' preference to learn. The more individualistic students like the learning style that involves doing, while the more collective students prefer the learning style that involves watching. They discovered that Japanese students like the style of learning-by-watching due to their collective traits. In comparison, the two other student groups from Australia and Belgium tend to be more individualistic, and therefore are more willing to pursue the style of learning-by-doing.
2. Methodology
The aim of this study is to explore the instrument and to test the predictive validity of Approaches and Study Skills Inventory for Students (ASSIST) on current and past MDAB students in UiTM Negeri Sembilan. It is hypothesized that there is significant positive correlation between Deep, Surface and Strategic approaches. The questionnaires were distributed to the current and past MDAB students in UiTM Negeri Sembilan, comprising a total of 112 samples. One section out of four in ASSIST has been used, which is Approaches to Study. The scoring procedure for this section follows the rule of Likert scale- 1 (disagree) to 5 (agree). The items in Approaches to Study are grouped into three scales, which represent 3 approaches of learning (Deep, Surface and Strategic). Deep Approach scale contains four subscales which are seeking meaning (SM), relating idea (RI), use of evidence (UE) and interest in ideas (II). Surface Approach scale also includes four items of subscales. The items are lack of purpose (LP), unrelated memorising (UM), syllabus boundness (SB) and fear of failure (FF). The Strategic Approach scale consists of five items, which are organised study (OS), time management (TM), achieving (A), alertness to assessment demands (AD) and monitoring (M). Table 1 shows the distribution of test items according to subscales.
The analysis has been examined through Path Diagram Analysis by using SPSS and AMOS software. Path diagram will display the full set of relationships among the model that has been constructed. The programme used is Analysis of Moment Structures (AMOS). Path analysis is a method for representing a set of regression equations by way of diagrams. According to Lay (2010), using path analysis is very helpful in analysing direct and indirect effects.
Goodness-of-fit of the structural model can be measured by using Absolute Fit Index (AFI), Incremental Fit Index (IFI) and Parsimonious Fit Index (PFI). AFI measures the overall goodness-of-fit for both the structural and measurement model. On the other hand, ICI measures goodness-of-fit that compare the current model to a
specified null model to determine the degree of improvement over the null model. Meanwhile PFI measures the overall goodness-of-fit, which represents the degree of fit per estimated coefficient. It attempts to correct any 'over-fitting' of the model and evaluates the parsimony of the model compared to goodness-of-fit.
Table 1. Distribution of ASSIST Items According to Subscales
Subscales Item No. No. Of Item
Seeking Meaning (SM) 4, 17, 30, 43 4
Relating Idea (RI) 11, 21, 33, 46 4
Use of Evidence (UE) 9, 23, 36, 49 4
Interest in Ideas (II) 13, 26, 39, 52 4
Lack of Purpose (LP) 3, 16, 29, 42 4
Unrelated Memorising (UM) 6, 19, 32, 45 4
Syllabus Boundness (SB) 12, 25, 38, 51 4
Fear of Failure (FF) 8, 22, 35, 48 4
Organised Study (OS) 1, 14, 27, 40 4
Time Management (TM) 5, 18, 31, 44 4
Achieving (A) 10, 24, 37, 50 4
Alertness to Assessment Demands (AD) 2, 15, 28, 41 4
Monitoring (M) 7,20,34,47 4
Total 52
Path coefficient can be calculated by the variance of observed variables and the significance of the coefficient is estimated by using standard error of covariance between the variables. In order to test the null hypothesis two, the Critical Ratio (CR) will be used. Critical Ratio value is obtained by dividing estimates of regression weights with standard error.
3. Result
Table 2 shows the descriptive scores for each variable. The skewness statistics for all variables, fall between -0.032 and -0.742. The measure between -1 and 1 is considered normally distributed. Hence, the data in this study meets the required assumption for statistical analysis. The next step is to measure reliability by using Cronbach's alpha. The value of 0.6 or higher provides a reliable measure of internal consistency. Here we found that six out of thirteen subscales of Cronbach's alpha are above 0.6 and the rest are close to 0.6. Missing Value is very small, which is one.
In order to obtain the statistical measure required in this study, the AMOS model was developed. Goodness-of-fit results are as shown in Table 3, which reveal that the model is fit and consistent. The p-value for chi-square statistic, which exceeds the predetermined significant level of p=0.05 shows that there is no significant difference between the population and sample covariance. The results from incremental fit index with values of 0.95 and greater are indicative of good fit.
Further analysis was conducted to identify significant paths in the model. Circle represents latent variables and rectangles represent measured variables. The following path diagram illustrates the three factors model, where the numbers on arrows from latent variables to observed variables are factor loadings or regression weights. We choose the maximum likelihood estimation because our data are normally distributed. The theoretical model and regression weights estimates are presented in Figure 2.
Table 2. Reliability, Normality and Missing Value Statistics
Mean Statistic Std. Deviation Statistic Skewness Statistic Kurtosis Statistic Cronbach's alpha % Missing value
SM 15.0536 2.09607 -.490 .457 0.522 0
RI 14.4911 2.20154 -.032 .078 0.630 0
UE 14.9196 2.31773 -.577 .246 0.627 0
II 15.1339 2.21581 -.283 .182 0.594 0
OS 14.0446 2.31092 .119 -.004 0.553 1
TM 15.5982 2.25197 -.319 .159 0.687 0
AD 15.4018 2.34985 -.742 .762 0.643 0
A 15.4375 2.35873 -.631 .395 0.632 0
M 12.7054 2.71678 .202 .086 0.593 0
LP 13.6786 2.49762 -.078 -.096 0.512 0
UM 14.4464 2.30877 -.118 -.190 0.547 0
SB 15.0536 2.09607 -.490 .457 0.522 0
FF 14.4911 2.20154 -.032 .078 0.630 0
Table 3. Goodness-of-Fit, N=112
Goodness-of-Fit
Coefficient / Index
Strategic
Surface
Absolute Fit Index Chi-Square
Degree of Freedom, df
Significance level for chi-Square, p(>. 05)
Incremental Fit index
IFI (0<x<1)
CFI (0<x<1)
TLI (0<x<1)
2.688 2 .261
.977 .930 .970
.339 2
.998 .990 .980
.000 2 .000
.980 .979 .937
Straight arrow in the diagram shows the regression weights and curved arrow shows the covariance's between constructs. The correlation between Deep and Strategic Approach among MDAB students is very high, which is 0.926. However the moderate correlation for Surface-Strategic and Deep-Surface are found with correlation coefficients of 0.346 and 0.509 respectively. All correlation estimates are positive.
Table 4 below shows the estimates of regression weights and standardised estimates to identify significant paths in the model presented in Figure 2. It appears along with standard errors, critical ratios and p-value. Standardised estimates can also be interpreted as the correlation between the observed variables and those factors. The factor loadings appear to be large in this model. R2 is a factor loading squared. It means the extent of that factor can explain the variable in a manifest variable. For instance, latent variable Deep explains about 67 percent of variance in SM. The p-values show a significant hypothesised path at 95% confidence level, since the predetermined significance level of p = 0.05 value of CR is outside ± 1.96 of rejection H0 region.
Fig. 2. Specification of ASSIST Model with Estimated Model Parameters
Table 4.Maximum Likelihood Estimates Regression Weights
Estimate Standardized Estimates S.E. C.R. P
SM <--- Deep .631 .727 .076 8.338 ***
RI <--- Deep .632 .722 .077 8.226 ***
UE <--- Deep 1.000 .853
II <--- Deep .896 .809 .091 9.794 ***
LP <--- Surface 1.000 .855
SB <--- Surface .593 .578 .178 3.333 ***
FF <--- Surface .797 .823 .165 4.819 ***
TM <--- Strategic .582 .607 .085 6.859 ***
MO <--- Strategic .957 .839 .085 11.281 ***
AD <--- Strategic .791 .759 .084 9.470 ***
AC <--- Strategic 1.000 .893
4. Conclusion
We tested the predictive validity of ASSIST on the current and past MDAB students in UiTM Negeri Sembilan. The findings of this study show that ASSIST is an appropriate instrument and the results are valid as indicated by the scores obtained in the main scales and subscales. Thus, ASSIST can be used as a valuable
instrument to access students' approaches to learning for MDAB students in Negeri Sembilan. We also found that there is a significant and positive correlation between Deep, Surface and Strategic approaches. However, further research with larger sample size should be examined. The study can also investigate the relationship between approaches to learning, personality and students' academic grades. Furthermore, it will ensure that weak students are more easily identified and the appropriate actions can be taken.
Acknowledgements
We are grateful to Assoc. Prof. Zaluddin Hj. Sulaiman, the Rector of UiTM Negeri Sembilan for budget approval and moral support. We also would like to thank MDAB and ex-MDAB students for filling in the questionnaires.
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