Effects of Problem-based Learning Approach on Cognitive Variables of University Students

(1)

Procedia - Social and Behavioral Sciences 46 ( 2012 ) 3146 – 3151

*_{Corresponding Author name. Tel.: +98-9126541828} E-mail address: p4ry4_f@yahoo.com

WCES 2012

Effects of problem-based learning approach on cognitive variables

of university students

Sahar Bayat

a*

, Rohani Ahmad Tarmizi

b

a,b_{Laboratory of Innovations in Mathematics Education, Institute for Mathematical Research,}

Universiti Putra Malaysia, Serdang, Selangor, MALAYSIA. Abstract

This study aimed to investigate the effectiveness of PBL approach on cognitive variables among postgraduate students who were taking Educational Statistic course. Two groups of students namely; PBL group and conventional instruction (CI) group were investigated on overall performance, conceptual knowledge, procedural knowledge, mental load, number of errors and students indicated a significant difference between these two groups (t[46]= -2.143, p<0.05), ( t [46]= -2.091, p<0.05). In addition, there is significant difference between these two groups on mental load during learning process (t [46] = -3.283, p<0.05).

2010 Elsevier Ltd.

Keywords: Problem-based learning; Statistic learning performance; Mental load; Instructional efficiency

1.Introduction

Many students understood mathematics as a boring subject because of the approach they learnt mathematics, which was to listen to what their teachers said, memorize what their teachers showed or demonstrated, and follow the teachers' procedures without necessarily trying to understand the concepts (Ridlon, 1999). Recent researches showed that students still do not perform well on mathematical tasks that require mathematical understanding and problem-solving skills, even though they have significantly improved their mathematics computational skills (Reese, Miller, Mazzee, & Dossey, 1997). One of the problems of mathematics education today may be that students are not developing the concepts and understanding to build upon for the more complicated mathematics that follows. For improving mathematics education an attempt must be made to find instructional practices which promote conceptual development, procedural knowledge, problem solving, and higher level thinking. For enhancing students' understanding, current research has recommended that mathematics teachers should build students' learning from the students' prior knowledge (e.g., Hiebert, Carpenter, Fennema, Fuson, Wearne, Murry, Oliver, & Human, 1997; Hiebert, 1998). The effects of constructivism on teaching and learning of mathematics have been discussed for years. In constructivism learning is an active, constructive process and constructivism is a theory that believes learning will be facilitated since it supplies learners with opportunities to construct knowledge in meaningful contexts of social environment; hence they have the chance to construct a comprehensive understanding.

(2)

PBL approach as an instructional approach can be supported based on constructivist views of learning in that aning with others, learners construct their own learning and make connections with prior notions and deal with content in a range of contexts. This approach gives a more active role to the students in their own learning in many classrooms in comparison with the traditional notions. In this approach, learning focuses on search for meaning (Young & Patterson, 2007). According to Besana, Fries and Kilibarda (2001) problem-based learning is an instructional method that challenges students to learn," working cooperatively in groups to seek solutions to real world problems. These problems are engaging students' curiosity and initiate learning the subject matter. The following figure illustrated the PBL approach steps which can use for the various fields such as business, dentistry, health sciences, law, engineering, education, and so on.

Figure 1: PBL Teaching and Learning cycle (http://pbln.imsa.edu/model/template/index.html)

PBL approach can be applied to any subject; it is also especially useful for the teaching of mathematics. In conducting PBL approach in mathematics teaching and learning, the teachers should give their students the opportunity to see mathematics as a living subject that is as an exploratory, dynamic, and evolving science. In PBL instructional approach, students move from being passive participants in education to active participants, determining for themselves what needs to be learned and how (Glasgow, 1997). Theoretical framework for PBL is provided by information-processing theory, collaborative learning, constructivist learning, and contextual learning theory. In a collaborative environment, groups of students work together in search for understanding, meaning or solutions or in creating their learning. The experience gives an opportunity to students in order to work together, develop the sense of teamwork and pride (Pewewardy, 2002; Reyes, 1991; Swisher, 1990).

The literature mentioned in this research is more about PBL mathematics class at schools. Williams (1998) carried out a study on 115 students which was comparing PBL with a traditional approach in a Texas Middle School science classroom. Williams (1998) stated that PBL significantly increased student achievement but did not

. Faaizah and Halimah (2008) conducted their research based on PBL courseware for the topic stats. The students worked in groups on a real-life scenario and delivered their project reports. It was found that the studen

addition, another research was conducted in Malaysia by Nurizzati (2008) to investigate the effects of PBL on form efficiency. The experiment was carried out on 53 form four students who were randomly selected. 29 students were exposed to the PBL instruction while another 24 students were put in the control group. The results of this research indicated that there was no significant difference in the mean scores of overall mathematics performance between PBL group and control group. However, there was a significant difference in mean mental load of PBL group and control group.

Understand the Problem Explore the Curriculum Resolve the Problem

a. Meet the Problem b. Know/Need to Know c. Define the Problem Statement

d. Collect Information e. Share Information

f. Generate Possible Solutions g. Devise the most appropriate Solution h. Present the Solution

(3)

Pass and Van Merrienboer (1993) introduced a measure of instructional efficiency conditions which can be computed based on performance test and mental load invested during learning process. The measure which was proposed under the cognitive load theory (Sweller, 2005) examined the effect of different instructional approaches on learning. Mental load refers to the total amount of controlled cognitive processing in which a subject is engaged (Paas & Van Merrienboer, 1993). Measuring mental load during learning phase is providing some interesting information for researchers and instructional designers. The instructional efficiency which measured in this study is combination of test performance with mental load invested in the learning phase instead of the test phase. The mean standardized test performance (P) and mental load during learning process (E) scores attained by learners in a certain condition are entered into the following formula:

The term performance in this study is an evaluation of the learning outcomes in terms of total performance score in the given tests. The variables of interest in this study are conceptual knowledge, procedural knowledge, overall performance, mental load, instructional efficiency and total number errors in performance tests. In this research, al knowledge refers to the ability of students to answer correctly to the first part of statistics tests (objective questions) which was based on understanding of statistics concepts. Procedural knowledge referred to ability to conduct the appropriate statistical procedures in solving the given problems were presented at the second part of the tests. In addition, procedural knowledge also referred to

their findings and sequences of procedure of solving statistic problems. On the other hand, mental load was measured as the perceived amount of mental load which a student spent while solving statistic assessment problems during learning processes by using the Paas mental effort rating scale (PMERS). The total number of errors was measured based on frequency of mistakes were done during statistics performance Tests. The number of errors was counted based on errors found in their solution steps of each problem.

2. Methodology

This study examined the effects of PBL approach on cognitive variables among postgraduate students who were taking Educational Statistic course. A quasi-experimental design comparing two groups of students namely, the PBL group and conventional instruction (CI) group were investigated. Students in PBL group learnt statistics based on problem-based learning approach and the CI group students learnt statistics based on series of lectures. Two sets

of test were conducted performance variables: Test I may be regarded as post-test 1 whilst

Test II as post-test 2. In this study, problem scenario, guided questions and assessment questions posed as platform towards collaborative work for students in each group. Their final goal is to produce a presentation of problem solution for the assessment questions which were as practice problems.

During the intervention, students were formed group of three and undergo cooperative, collaborative activities using the PBL Guided Learning Questions. Each module covers one topic in Educational Statistics for example Basics of Inferential Statistical Analysis, Test of Differences between Sample Mean and Population Mean, Test of Differences between Two Means, etc. Each PBL guided learning starts with a problem scenario. In processing and understanding the problem scenario, students were given guided questions and to be answered in the given order. Students were also given notes highlighting and focusing on the important concepts and the learning outcomes to be achieved. Students were also encouraged to source information on the website and any textbooks suggested for the course. Students were encouraged to answer the questions by using multiple resources prepared and suggested by the instructor. Students were asked to complete the first problem scenario with guided questions before proceeding to the second problem scenario. During this session the instructor act as facilitator providing guidance and monitoring the discussion based on the questions provided in the PBL Guided Learning Questions. In addition, they were given assessment questions and were told to work collaboratively on their own chosen time. The next session then starts with each group representative presenting the solution of the assessment questions. Students understanding and misunderstandings were clarified and concluded during this session.

(4)

by summing of scores of Test I and Test II. conceptual knowledge measured by summing of scores of part one of the Tests

was measured by summing of scores of part two of the Tests. In this study mental load was measured by a nine-point symmetrical rating-scale, ranging from very low mental load (1) to very, very high mental load (9), designed by Pass and van Merrienboer (1994) by translating the perceived amount of it into the numerical value, 1 to 9 during solving the assessment questions. The number of total errors was measured by total number of errors which students made in solving the problems of both Test I and Test II.

3. Findings

The findings of this study were mainly based on the experimental data gathered from the respondents.

and procedural knowledge between PBL and control group

, conceptual knowledge and

procedural knowledge in two groups of students, PBL group and CI group. Findings in this Table revealed that there is significant difference between means of overall performance of PBL group and CI group (t[46]= -2.14, rmance. In addition, there is also a significant difference between mean of procedural knowledge of students of PBL group and CI group (t [46] =

-2.09 re is

significant difference between mean of conceptual knowledge of students of PBL group and CI group in 9% level of significant, however the mean of conceptual knowledge of students of PBL (mean=65.4728) greater than mean of conceptual knowledge of CI students (mean=59.7647).

3.2 , Instructional efficiency and Total Number of Errors between PBL and control group

Table 2 , instructional

efficiency and the mean number of errors of PBL and CI group. Findings in Table 2 revealed that there is significant difference between means of mental load of students of PBL group and CI group (t[46]= -3.283, p<0.05). Hence, the students of CI group invested very more mental load rather than students of PBL group during statistics learning. In addition, the results indicates there is no significant difference between mean of instructional efficiency and mean of number of errors of students of PBL group and CI group, however the mean of instructional efficiency of students of PBL (mean=5.2615) is greater than mean of instructional efficiency of CI students (mean=4.6488) and mean number of errors of students of PBL group (mean=23.08) is less than mean number of errors of students of CI group (mean=28.92).

Group N Mean Std. Deviation Std. Error Mean t df Sig. (2-tailed) Mean Difference Overall-Performance CI 25 59.6000 20.38289 4.07658 46 .037 -1.048 PBL 23 70.0822 12.08737 2.52039 -2.14 Procedural-Knowledge CI 25 60.5787 23.27450 4.65490 -2.09 46 .042 -1.169 PBL 23 72.2753 13.87973 2.89412 Conceptual-Knowledge CI 25 59.7647 13.11822 2.62364 -1.70 46 .094 -5.708 PBL 23 65.4728 9.58268 1.99813

(5)

4. Discussion

University students often learn mathematics as traditional approaches which are usually lecture-centre. This study seeks effectiveness of new teaching approach (PBL) on some cognitive variables. Students of PBL group learnt statistics by using problem based learning approach which was based on scenarios, guided questions, assessment questions and group working. The guided questions and collaborative learning as two strategies played very important role in this new mode of learning. Students in CI group which was as control group learnt statistics based on lecture-centre. Learning in teams provide students with the opportunity to share their thoughts, check understanding, exchange ideas and communicate with other classmates. In collaborative learning environment, interaction among students helps to enhance their motivation in the lesson as they engage in activities that are more interesting and meaningful to them.

Research in PBL as a new mode of learning is not new. Although, it started in medical schools it has been studied and researched in other fields consisting education. On the whole, findings of this study showed that

Problem Based Learning approach, as a new approach, has positive performance because a

significant difference was found between PBL and CI group on their performance. On the other hand,

this approach has positive procedural knowledge and mental-load during the learning

process. However, there was no significant difference on conceptual knowledge of PBL and CI group. The results of this study revealed that the mental load of PBL and CI students during the assessment problems which as a part of learning process did differ. However, based on instructional efficiency index obtained, the PBL environment revealed that PBL group and control group (CI) result in no significant differential effect. This finding is consistent with findings of Candela (1999) who maintained that undergraduate students exposed to PBL intervention did not outperform students in traditional approach. However, in two other studies, Elshafei (1999) and Deveci (2002) conducted on secondary schools and primary schools students respectively indicated that PBL was more effective than the control group in increasing achievement. The instructional efficiency which measured in this study is adapted instructional efficiency. The difference between original instructional efficiency and adapted instructional efficiency is in mental load invested by students. In original instructional efficiency the mental load is invested in the test phase and in adapted instructional efficiency mental load is invested in learning phase. In this study stude ntal-load was measured during learning process via assessment problems. Based on cognitive load theory, much need to be investigated. Whilst guided questions during PBL activity should assist learning as scaffoldings which aid students understanding of statistical concepts, this evidently did not lend positive impact. It may be concluded that guided question in PBL environment had increased extraneous mental load hence exceeding its positive capability. Earlier study by Tarmizi and Sweller (1988) has also concluded that guidance in geometry

Table 2 Mental Load, Instructional Efficiency and Number of Errors

Group N Mean Std.

Deviation Std. Error Mean t

df Sig. (2-tailed) Mean Difference

Instructional-Efficiency CI 25 4.64 2.17 .43 -1.08 46 .28 -.61 PBL 23 5.26 1.70 .35 Mental-load CI 25 5.19 1.41 .28 -3.28 46 .00 -1.19 PBL 23 4.8 1.05 .21

Total Number of Errors CI 25 28.92 13.66 2.73

1.38 46 .173 5.83 PBL 23 23.08 15.54 3.24

(6)

also observed by Van Gog, Pass and Van Merrienboer (2004) and Sweller (2005). Future research focusing on improving mode of guidance during mathematical problem solving which align with cognitive load theory need to be undertaken.

References

Albanese, M. A. & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issue. Academic Medicine, 68, 52-81.

Cadela,L.L. (1999). Problem based learning versus lecture: Effects on multiple choice test scores in associate degree nursing students. Dissertation Abstracts International, 60(5-A), 1419.

Capon, N., and Kuhn, D. "What's So Good About Problem-Based Learning?" Cognition and Instruction, 22 (1) (2004), 61-79.

Davidson, J. E. & Sternberg, R. J. (1998). Smart problem solving: How meta-cognition helps. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 47-68). Mahwah, NJ: Lawrence Erlbaum.

Dolmans, D., De Grave, W., Wolfhagen, I., & Van Der Vleuten, C. (2005). Problem based learning: Future challenges for educational practice and research. Medical Education, 39(7), 732-741.

Duch, B.J.,Groh, S.E.,&Allen, D.E.(2001). Why problem-based learning? A case study of institutional change in undergraduate education. In B.Duch, S.Groh, & D.Allen (Eds.). The power of problem-based learning (pp.3-11). Sterling, VA:Stylus.

Devieci, H.(2002). The Effect of problem based learning to attitudes, success and retention in social studies. Unpublished PHD dissertation. Eskisehir: Anadolu University Graduate School of Educational Sciences.

Dochy, F., Segers, M., Van den Bossche, P., & Gijbels, D. (2003). Effects of problem-based learning: a meta-analysis. Learning and Instruction,

13, 533-568.

Evensen, D. H. & Hmelo, C. E. (Eds.). (2000). Problem-based learning: A research perspective on learning interactions. Mahwah, NJ: Lawrence

Erlbaum Assoc.

Elshafei,D.L.(1999).A comparison of problem based and traditional learning in algebra II. Dissertation Abstract International, 60 (1-A), 0085. Faaizah, S. and Halimah, BZ.2008. Evaluating the Effectiveness of Problem-Based Learning in Science Teaching: Hybrid Approach.

Proceeding of International Symposium on Information Technology Malaysia1, 1-7.

Glasgow, N. A. (1997). New curriculum for new times: A guide to student-cantered, problem-based learning.

Gallagher,S.,Stepien, W., & Rosenthal, H.(1992). The effects of problem based learning on problem solving. Gifted Child Quarterly.36(4):195- 200.

McCombs, V.(2000).What is problem-based learning. USA: Stanford University. M. Cazzola. Problem-based learning and teacher training in mathematics. submitted, 2008.

Ngeow, K. & Kong, Y. S. (2001). Learning to learn: Preparing teachers and students for problem-based learning (Report No. EDO-CS-01-04). Bloomington, IN: ERIC Clearinghouse on Reading, English, and Communication. (ERIC Document Reproduction Service No.

ED457524).

Nur Izzati A. (2008). The Effects of Problem Based Learning on Mathematics Performance and Affective Attributes in Learning Statistics at Form Four Secondary Level. Procedia Social and Behavioral Sciences, ICMER2010. Vol 8-2010, 370 376.

l achievement through the use of motivational strategies.

Pass,f.,& van God, T.(2006). Optimizing worked example instruction: Different ways to increase germane cognitive load. Learning and instruction, 16, 87_91

Pass, F.& van Merrienboer, J.J.G.(1993). The efficiency of instructional conditions: an approach to combine mental effort and performance measures. Human factors, 35, 737_743

Sweller, J.(2005). Implications of cognitive load theory for multimedia learning. In R.E. mayer (Ed.), Cambridge handbook of multimedia learning (pp. 19-30). New York: Cambridge University Press.during problem solving: effects on learning. Cognitive science, 12, 257_285.

Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22, 129 184

Tarmizi R.A.. & Sweller, J. (1988) Guidance during Mathematical Problem Solving. Journal of Educational Psychology, Vol. 80, No. 4, pp 424-436. American Psychological Association Inc. USA.

Williams, D., Hemstreet, S.,Liu, M., & Smith, V. (1998). Examining how middle school students use Problem-Based Learning software. In Ed Media/Ed-Telecom 98, World Conference on Educational Telecommunications. Austin, TX: University of Austin, Department of Curriculum and Instruction.

Young, L. & Paterson, B. (2007). Teaching nursing: Developing a student-cantered learning environment. Philadelphia: Lippincott Williams & Wilkins.