Tun Hussein Onn University of Malaysia (UTHM) has been implementing

Problem-Based Learning (PBL) to some degree in various subjects. However, to this day no empirical data has been gathered on the effectiveness of PBL as a methodology to develop self-directed learning (SDL) skills. The purpose of this study is to investigate self-directed learning readiness (SDLR) among UTHM students exposed to vaiying PBL exposure intensity. SDLR was measured using the modified version of Self-Directed Learning Readiness (SDLRS). Participants in this study were first-year undergraduate students at UTHM. The instrument was administrated to students in Electrical and Electronics Engineering, Civil and Environmental Engineering, and Technical Education (N=260). Data were analyzed using descriptive and inferential statistical techniques with analysis of variance (ANOVA) and the independent /'-test for equal variance for hypotheses testing. The results of this study indicate that overall SDLR level increase with PBL exposure up to exposure intensity twice, beyond which no increase in SDLR was observed with increase in PBL exposure. Within the same academic programme, results did not

show a statistically significant difference of SDLR level between groups exposed to varying PBL exposure intensity. However, significant difference was found in some dimensions of the SDLR for the Technical Education students. Within the same education background, results did not show a statistically significant difference of SDLR level between groups exposed to varying PBL intensity. However, significant difference was found in some dimensions of the SDLR for students with both Matriculations and STPM background. A statistically significant difference of SDLR level was found between Electrical Engineering and Technical Education students

for exposure once and in some SDLR dimensions. No statistically significant difference was found between students from different academic programme for exposure twice or thrice. The data supports the conclusion that SDLR level increases with increase in PBL exposure intensity up to a certain extent only, beyond which no increase of SDLR can be observed. The data also suggest that only certain dimensions of the SDLR improve with increased exposure to PBL

Zainal Alam, Siti Nurzakiah

English

Tun Hussein Onn University of Malaysia (UTHM) has been implementing 

Problem-Based Learning (PBL) to some degree in various subjects. However, to this 

day no empirical data has been gathered on the effectiveness of PBL as a 

methodology to develop self-directed learning (SDL) skills. The purpose of this 

study is to investigate self-directed learning readiness (SDLR) among UTHM 

students exposed to vaiying PBL exposure intensity. SDLR was measured using the 

modified version of Self-Directed Learning Readiness (SDLRS). Participants in this 

study were first-year undergraduate students at UTHM. The instrument was 

administrated to students in Electrical and Electronics Engineering, Civil and 

Environmental Engineering, and Technical Education (N=260). Data were analyzed 

using descriptive and inferential statistical techniques with analysis of variance 

(ANOVA) and the independent /'-test for equal variance for hypotheses testing. The 

results of this study indicate that overall SDLR level increase with PBL exposure up 

to exposure intensity twice, beyond which no increase in SDLR was observed with 

increase in PBL exposure. Within the same academic programme, results did not 

show a statistically significant difference of SDLR level between groups exposed to 

varying PBL exposure intensity. However, significant difference was found in some 

dimensions of the SDLR for the Technical Education students. Within the same 

education background, results did not show a statistically significant difference of 

SDLR level between groups exposed to varying PBL intensity. However, significant 

difference was found in some dimensions of the SDLR for students with both 

Matriculations and STPM background. A statistically significant difference of SDLR 

level was found between Electrical Engineering and Technical Education students 

for exposure once and in some SDLR dimensions. No statistically significant 

difference was found between students from different academic programme for 

exposure twice or thrice. The data supports the conclusion that SDLR level increases 

with increase in PBL exposure intensity up to a certain extent only, beyond which no 

increase of SDLR can be observed. The data also suggest that only certain 

dimensions of the SDLR improve with increased exposure to PBL

UTHM Institutional Repository

Relationship between problem-based learning experience and self-directed learning readiness

International Seminar on the Application of Science & Mathematics 2011 ISASM 2011    1 MODIFICATION OF CONTINUOUS AND BINARY ICU DATA IN FCRM MODELS  Mohd S aifullah Rusiman1, Robiah Adnan2, Efendi Nasibov3 and Kavikumar Jacob4  1, 4Department of Mathemat ics, Faculty of Science, Technology and Human Development,  Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia  2Faculty of Science, Universit i Teknologi Malaysia  3Faculty of Science and Arts, Dokuz Eylul University, Turkey   1saifulah@uthm.edu.my, 2robiaha@utm.my, 3efendi.nasibov@deu.edu.tr, 4kavi@uthm.edu.my  This research is an attempt to present a proper methodology in data modificat ion by using analytical hierarchy process (AHP) technique and fuzzy c-mean (FCM) model. The continuous data were built from b inary data using analytical hierarchy process (AHP). Whereas, the binary data were created from continuous data using fuzzy c-means (FCM) model. The models used in this research are fuzzy c-regression models (FCRM). A case study in scale of health at an intensive care unit (ICU) ward using the AHP, FCM model and FCRM models  was carried out. There are six independent variables involved in this study. There are four cases considered as a result of using AHP technique and FCM model toward independent data. After comparing the four cases, it was found that case 4 appeared to be the best model, having the lowest mean square error (MSE). The orig inal data have the MSE value of 97.33, while the data of case 4 have MSE by 83.48. Th is means that the AHP technique can lower the MSE, while the FCM model cannot lower the MSE in modelling scale of health in the ICU. In other words, it can be claimed that the AHP technique can increase the accuracy of modelling prediction.  Keywords: Analytical hierarchy process (AHP), fuzzy c-means (FCM) model, fuzzy c-regression models (FCRM), mean square error (MSE)  1. INTRODUCTION Regression model has become one of the standard tools in data analysis since the mathematical equation from its analysis could explain the relationship between the dependent and independent variables. It is available in computer packages, easy to interpret and has been widely used in applied sciences, economic, engineering, computer, social sciences and other field. Fuzzy modelling has become popular for the past few years because it describes complex systems better. The fuzzy c-mean (FCM) model proposed by Bezdek in 1981 develops hyper-spherical-shaped clusters. In contrast, the fuzzy c-regression models (FCRM) proposed by Hathaway and Bezdek [1], develop hyper-plane-shaped clusters. Analytical hierarchy process (AHP) has been proposed by Thomas L. Saaty in 1977 in handling factor weights due to a lack of historic information. It has been widely used in decision making, since it includes the  2 natural feelings of human beings.Many researchers employ AHP technique in handling data mining problem [2]. The intensive care unit (ICU) plays an important role in the medical care sector not only for the critically ill, who makes up 5% of inpatients, but also in terms of generating a major contribution of health care funds. The United States health care industry makes up 15-20% total hospital cost. In 1968, the first ICU in Malaysia was established. Intensive care has then developed rapidly and it is now available in all tertiary care hospitals and selected secondary care hospitals. The National Audit on Adult Intensive Care Units in Malaysia in 2002 is modeled on the UK experience in 1994 and coordinated by a national committee comprising of senior intensive care specialists in the Ministry of Health. This audit unit develops a national database to assess fundamental aspects of intensive care functions within a hospital. The clinical indicators developed by ACHS (The Australian Council on Healthcare Standard) are useful tools for clinicians to flag potential problems and areas for improvement [3]. Currently, there was common method used in ICU involves logistic regression [4]. Only Pilz and Engelmann [5] did a basic fuzzy rule to determine the medical decision in ICU. This work inspires us to do work in fuzzy model into ICU area that could give a challenge to this study. The first research on mortality rates in Malaysian ICU has been done at a general hospital in Ipoh, involving only a logit model [3]. The second research is continued by Mohd Saifullah Rusiman et al. [6] on the analysis of logit, probit and linear probability models. As a comparison among the three models, logit model has been appeared to be the best model. The objective of this research is to explore data modification using AHP technique and FCM model in scale of health at the ICU. The other objective is to make a comparison among the beginning data (without any method), AHP technique, FCM model or any combination of methods which are applied to data in order to find the best model. So, we can make recommendation based on this data mining method in predicting scale of health in the general hospital. 2. MATERIAL In this study, the data were obtained from the intensive care unit (ICU) of a general hospital in Johor . The data obtained were classified as a cluster sampling. It involves 1311 patients in the ICU within the interval of January, 2001 to August, 2002. The dependent variable is scales of health or score of SAPS II discharge from hospital (s2sdisc). There are six independent variables considered in this study which are sex, race, organ failure (orgfail), comorbid diseases (comorbid), mechanical ventilator (mecvent) and score of SAPS II admit (s2sadm). The s2sdisc and s2sadm scores are 15 accumulated values for heart rate, blood pressure, age, body temperature, oxygen pressure, urine result, urea serum level, white blood count, potassium serum level, sodium serum level, bicarbonate serum level, bilirubin level, glasgow coma score, chronic illness and type of admittance as proposed by Le Gall et al. [7].     International Seminar on the Application of Science & Mathematics 2011 ISASM 2011    3 3. METHODOLOGY 3.1 AHP technique The AHP technique is a complete decision making process that permits more complete consideration of multi-factors/criteria. The AHP procedure involves three steps as;  Step 1: Establish the decision hierarchy  In this step the decision maker must identify the overall decision, the factors that must be weighted or used to make the decision and the alternative choices from which a decision it to be made. Once these are identified they are placed in a decision hierarchy.  Step 2: Compute the weighted of alternatives In this step the decision maker or expertise must compare each alternative with all other alternatives for one factor at one time. The rating measure scale used to rate the alternatives on a range from 1 to 9 as it relates to each of the factors. The weighted or probabilities obtained from a paired comparison matrix, summing to 1. Step 3. Compute the weighted of factors  In this step the decision maker uses the previously determined comparison ratings to compute a set of priorities for the individual factors. This involves several small computation sub-steps where the probabilities or weighted obtained from a paired comparison matrix with the total of one [2]. 3.2 FCM model In FCM clustering, based on the Dunn [8] and Bezdek [1] algorithm, we have to minimise the criterion J in (1),  cjNiijwij duJ1 12 , w > 1     (1) where iju is the membership values, 2ijd  is 211/NiwijNiiwiji uxux or the Euclidean distances, N  is the number of objects, c is the number of clusters and  w  is the weight or fuzzifier. In order to minimize (1), we have to; (a). Fix the value of c. Initialise membership values U or iju at random. Choose the termination tolerance δ > 0.  (b). Update Euclidean distances, ijd  for given U by computing the weighted averages for each group. (c). Update membership values,  ,1112Ckwikijijddu  for w > 1       otherwise   0)min( if    1 ikijijddu    for w = 1       (2)  4 (d). Calculate  the criterion J in (1)  and check  for convergence. If || newold JJ  < δ stop the iteration, else go to step (b).  3.3 FCRM models There are no conditions needed in FCRM models. Based on the algorithm in Hathaway & Bezdek [1], Abonyi & Feil [9] and Kung & Su [10], we have to, (a)  Fix the number of cluster c, c  2. Choose the termination tolerance δ > 0. Fix the weight, w,  w > 1 (a common choice in practice is to set w = 2) and initialise the initial value for membership function matrix, (0)U satisfying (4). (b)  Estimate  c  ,...,1   simultaneously  by   modifying   the  FCM   algorithm.   If  the regression functions );( ii xf are linear in the parameters i , the parameters can be obtained as a solution of the weighted least squares,  YWXXWX iTbbiTbi1][          (3)  where ]1,[XXb .  (c)   Calculate the objective function: ][}]{,U[1 1iijcidjwijiw EuE         (4) where  (i)   iju is membership degree  (i = 1,…, c ; j = 1,…, N). (ii) ][ iijE  is the measure of error with 2||);(|| ][ ijijiij XfYE . The most  commonly used is the squared vector Euclidean norm for );( ijij XfY .  (d)   Do iterations in order to minimize the objective function in (4). Repeat for l = 1, 2,…,   until   |||| )1()( ll UU . Next, follow the steps below: Step 1 : Calculate model parameters )(li to globally minimize (4). Step 2 : Update U with ][)1(liijij EE , to satisfy: jjjckwkjijlijI  iIIEEu and  for       ,                     0for      ,   1112)(        (5)             0  and  1 |    where ijj Ec  iiI  until   |||| )1()( ll UU . The mean square error (MSE) is used as follow, 2ˆ1ii YYNMSE           (6) where iY  denotes the real data, Yˆ  represents the predicted value of iY  and N is the number of data.  International Seminar on the Application of Science & Mathematics 2011 ISASM 2011    5 4. DATA ANALYSIS 4.1 AHP technique The AHP technique is applied to organ failures variable (orgfail). This independent variable only has two binary data, that is, patients who have or do not have organ failures. This technique will fuzzify the binary data of organ failures to be a continuous data within the interval [0, 1].  Organ failures are divided into 6 types which are: (A) Respiratory failure, (B) Cardiovascular failure, (C) Neurological failure, (D) Renal failure, (E) Hepatic failure and (F) Haematological failure. Referring to the expert physicians in the general hospital, they stated that B and D have a twice higher probability that contribute to high mortality if compared to the A and F. In fact, the A and F have a twice higher probability if compared to the C and E. However, B and D have the same weightage. The same weightage are also given to the A and F. C and E also receive the same weightage. The paired comparison matrix and probabilities (weighted) are shown in Table 1.   Table 1. The paired -comparison  matrix and  weighted  fo r o rgan failu res   B D A F C E Total Weighted  B 1 1 2 2 4 4 14 0.2857 D 1 1 2 2 4 4 14 0.2857 A ½ ½ 1 1 2 2 7 0.1429 F ½ ½ 1 1 2 2 7 0.1429 C ¼ ¼ ½ ½ 1 1 3.5 0.0714 E ¼ ¼ ½ ½ 1 1 3.5 0.0714  4.2 FCM model In order to get categorical data of s2sadm with ‘1’ and ‘2’ coded, we have to cluster s2sadm data based on FCM clustering algorithm. The data for cluster 1 with 860 data ranges from 0 to 43 whereas the data for cluster 2 with 443 data ranges from 44 to 126. This is the same as the cluster given by the doctors who indicated that the s2s score over 43 is classified as a bad condition.   4.3 FCRM models In this study, there are four cases considered as in Table 2 as a result of using AHP and FCM model toward independent data. The four cases involves six variables with different combination of variable types in each case were considered in order to find the best model using FCRM models. The variables involved are sex (1x  is binary), race (2x  is category), orgfail ( 3x  is binary or continuous), comorbid ( 4x  is binary), mecvent ( 5x  is binary) and s2sadm ( 6x  is binary or continuous). Case 3 is the beginning data without any modification being carried out.    6 Table 2 : Different case of mult ivariate data (Y vs 1x , 2x , 3x , 4x , 5x , 6x  ) Case 1 2 3 4 1x  B B B B 2x  Ca Ca Ca Ca 3x  B Co  B Co  4x  B B B B 5x  B B B B 6x  B B Co  Co  MSE for MLR(SV) 632.14 (VA) 526.40 (VA) 498.29 (VB) 463.10 (VA) MSE for FCRM(AV) 116.05 114.71 98.28 84.01 MSE for FCRM(SV) 121.92(VA) 97.29(VA) 97.33(VB) 83.48(VA) Note:  B:Binary,  Ca:Category,  Co:Continuous,   AV:All variables  SV:Significant variables(VA, VB) VA: 4 Variables chosen -1x , 3x , 4x  & 6x      VB: 5 Variables chosen - 1x , 3x , 4x , 5x  & 6x   There are four cases considered as a result of combination cases with/without using AHP technique and/or FCM model toward independent data. Table 3 shows that case 4 is the best case with the lowest MSE, that is, when 1x  is binary, 2x  is category, 3x  is continuous, 4x  is binary, 5x  is binary and 6x  is continuous. The MSE value for FCRM models for case 4 is 84.01 (all variables) and 83.48 (significant variables - 1x , 3x , 4x , 6x ). The MSE value for significant variables shows better result. The MSE value for case 3 (original data) is 97.33, while the MSE value for case 4 is 83.48. The MSE values for the other cases are 97.29 and 121.92. In conclusion, case 4 is the best case in which data modification involves only the orgfail variable. These chosen models ( 6431  , , ,  vs xxxxy ) are represented in (7) with two clusters.  Cluster 1 2967.611721.06925.48113.124644.2 THEN          is and is and is andisIF:643111661441331111xxxxy   A x A x A x  A  x R Cluster 2 8764.44788.00093.48245.12257.1 THEN           is and is and is andisIF:643122662442332112xxxxy  A x A x A x  A  x R            (7)  5 CONCLUSION In this research, data modifications were done to the case study in ICU where the binary data (s2sadm variable) were built from continuous data using FCM model, whereas the continuous data (orgfail variable) were created from binary data using AHP technique. There are four cases considered as a result of combination cases International Seminar on the Application of Science & Mathematics 2011 ISASM 2011    7 with/without using AHP technique and/or FCM model toward independent data. After comparing the four cases, it was found that case 4 appeared to be the best model, having the lowest MSE of 83.48, while the original data have the MSE value of 97.33. This means that the AHP technique can lower the MSE value, while the FCM model cannot lower the MSE in modelling scale of health in the ICU. In other words, it can be declared that the AHP technique can increase the accuracy of modelling prediction and should be used as a reference in hospitals to improve data accuracy. References [1]   [2]    [3]    [4]    [5]    [6]    [7]    [8]   [9]   [10]   Hathaway, R. J. & Bezdek, J. C. (1993). Switching  Regression  Models  and  Fuzzy Clustering. IEEE Transactions on Fuzzy Systems, 1: 195–204.  Saaty, T. L., Peniwati, K. & Shang, J. S. (2007). The Analytic Hierarchy Process and Human Resource Allocation: Half the story. Mathematical and Computer Modelling, 46 (7-8): 1041–1053.  The Committee for National Audit on Adult Intensive Care Units (2002).  Protocol : National Audit on Adult Intensive Care Units.  Colpan, A., Akinci, E., Erbay, A., Balaban, N. & Bodur, H. (2005). Evaluation of Risk Factors for Mortality in ICUs: A Prospective Study from a Referral Hospital in Turkey.  American Journal of Infection Control, 33: 42-47.  Pilz, U. & Engelmann, L. (1998). Integration of Medical Knowledge in an Expert System for Use in Intensive Care Medicine. Fuzzy and Neuro-Fuzzy Systems in Medicine, 2: 290-315.  Rusiman, M. S., Mohd Daud, Z. & Mohamad, I. (2004). The Comparison between Logit, Probit and Linear Probability Model toward Mortality Rate at ICU General Hospital. Jurnal Statistika Universitas Islam Bandung, 4: 129-138.    Le Gall, J-R., Lemeshow, S., Saulnier, F (1993). A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. The Journal of the American Medical Association , 24: 2957-2963.  Dunn, J. C. (1973), A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, 3: 32-57.  Abonyi, J.  & Feil, B. (2007). Cluster analysis for data mining and system identification, USA: Springer.  Kung, C. C. & Su, J. Y. (2007). Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion, IET Control Theory Application, 1: 1255–1265.   

Relationship between problem-based learning experience and self-directed learning readiness

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