




























































































Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
article of journal
Typology: Thesis
1 / 124
This page cannot be seen from the preview
Don't miss anything!
Marniati, SE., M.Kes
Juli Dwina Puspita Sari, SE., M.Bus (Adv)
Zalfie Ardian, S.Kom., M.Eng
Ferdi Nazirun Sijabat, SE., M.Sc. Mgt
Assoc. Prof. Dr. Rizal Afande Che Ismail Universiti Malaysia Perlis
Assoc. Prof. Dr. Mohd. Mustafa Al Bakri Abdullah Universiti Malaysia Perlis Prof. Dr. H. Zulkarnain Lubis Universiti Malaysia Perlis Assoc. Prof. Dr. Aminul Islam Universiti Malaysia Perlis
Dr. Abdullah Osman Universiti Malaysia Perlis
Dr.Ummi Naiemah Sarahi Universiti Malaysia Perlis Dr. Mohd Suberi Ab. Halim Universiti Malaysia Perlis
Engr. Dr. Mohammad Harith Amlus Universiti Malaysia Perlis
Dr. Tunku Salha Tunku Ahmad Universiti Malaysia Perlis
Mohd. Zakime Mat Junoh Universiti Malaysia Perlis Tuan Haji Rusli Abd. Hamid Universiti Malaysia Perlis Ain Zaraini Zin Aris Universiti Malaysia Perlis Syahida Kamil Universiti Malaysia Perlis Hussen Nasir Universiti Malaysia Perlis Mohammad Safizal Abdullah Universiti Malaysia Perlis Mohammad Shariff Ismail Universiti Malaysia Perlis Assoc. Prof. Dr. Yoshifumi Harada Universiti Malaysia Perlis Dr. Suhairimi Abdullah Universiti Malaysia Perlis Dr. Huzili Hussin Universiti Malaysia Perlis
Rafiza Abdul Razak Universiti Malaysia Perlis Zarina Yahya Universiti Malaysia Perlis Liyana Jamaluddin Universiti Malaysia Perlis Professor Dr. Md. Golam Rahman Daffodil International University Prof. Dr. Muhammad Mahboob Ali Daffodil International University Professor Dr. Yousuf Mahbubul Islam Daffodil International University Professor Dr. Syed Akhter Hossain Daffodil International University Profesor Dr. Md. Fayzur Rahman Daffodil International University Professor Dr. A. K. M. Fazlul Haque Daffodil International University
Table Of Contents
Universitas Ubudiyah Indonesia ISSN : 2442-
material without the use of visual media [10]; [11]; [12]; [8]. The absence of instructional media also cause passive learning and even boring [13], which further result in lack of the spirit and motivation to learn [14]; [15]; [16]. Some differences in cognitive abilities brought about the low achievement of the students [17]. These problems must be overcome in order to improve the intended results, and to meet the standards competence of stakeholders as well. It can be obtained through learning using multimedia. The use of multimedia will enable students to understand both the conceptual and abstract teaching materials [18]. Komaro, et al [19] has built a multimedia animation (MMA) and has succeeded to improve students understanding about the atomic structure of the crystal in technology field. The electricity learning of multimedia simulation model has proved itself to be able to facilitate students in comprehending conceptual and abstract teaching material [20]; [1]; [21]. The results of the above studies demonstrate that the use of multimedia can improve learning outcomes. This study aimed to assess the effect of learning using multimedia Exploratory Tutorials Learning (ETLn) towards learning outcomes of the students in Electrical Power Generation subject, based on differences in spatial ability.
B. Literature Review Learning outcomes are greatly determined by abundant factors, one of them is the availability of multimedia learning. Multimedia learning encourages students to understand their learning material more easily. Multimedia teaching materials can visualize conceptual and abstract and complex events to be simpler and is easily understood. Information processing theory [22], is the basis of this study [23]; [24]. Cognitive processes takes place in the human brain starting from the reception, processing and storage of information and the recall of the brain [25]. There are two information processing theory, namely store structure model by Atkinson & Shiffrin and level of processing model of Craik and Lockhart [26]. According Baddeley et al, [27] working memory has four main components, they are visuo-spatial sketchpad, episodic buffer, phonological loop and the central executive. In the context of learning, a person will be able to learn more easily when the material is presented in a visual and auditory rather than just through spoken language alone [28]. This corresponds to the dual-coding Paivio theory [29], that individual would be optimally receives learning materials if it involves seeing and hearing. Multimedia- based learning theory can be distinguished into two levels, namely the lower level and upper level. Lower level which deals with the theory of psychology includes memory system and cognitive processes, while the upper level with respect to the theory of multimedia design principles that include text, graphics, audio, animation and video [30]. Theories which are relevant to a lower-level is dual-code theory by Paivio and memory models by Baddeley. While in relation to the upper
Universitas Ubudiyah Indonesia ISSN : 2442-
theory is cognitive load theory by Sweller and cognitive theory of multimedia learning [27]; [29]. Multimedia learning will be effectively built if it rests on learning norms, such as Cognitive Theory of Multimedia Learning which combine Cognitive Load Theory by Sweller, Dual-coding Theory by Paivio, and Working Memory Models by Baddeley [31]. The combination of text, images and multimedia audio makes learning will surely improve learning outcomes, students’ interest, enthusiasm and motivation. Mayer stated the sequence of processing information in the model of the cognitive theory of multimedia learning as figure 1.
Figure 1. Cognitive theory of multimedia learning [32]
This theory emphasizes the four principles of study in the process of cognitive science, they are Dual channel, Limited capacity, Active processing, Transfer. Cognitive Load Theory becomes the basis of the learning process that the form of instructional should consider the cognitive load [18]; [33]. Cognitive load in working memory are caused by three sources: (1) Intrinsic cognitive load; (2) Extrinsic cognitive load, and (3) German cognitive load [34]; [35]. Cognitive load can be reduced in the process of learning with multimedia built on the principles of design of multimedia [28]. Multimedia is a tool in learning process used to achieve paramount results. Multimedia which contains teaching materials organized in the form of modules that are easily understood [36]. Multimedia which is used in this study is an Exploratory Tutorial Learning models [37]; [38]. Learning materials in this principle are organized into modules together with comprehension principle, and students’ flexibility to access teaching materials in link-access to the database as it is seen in figure 2.
Universitas Ubudiyah Indonesia ISSN : 2442-
Learning outcomes can be divided into two abilities, namely memory recall, and memory retention [48]; [49], and this study examines two kinds of learning outcomes. Memory recall is the ability of the student obtained through tests that is carried out after learning process is complete (immediate post-test), whereas memory retention [50], is obtained by students through exams conducted after two weeks following the learning process (delayed post-test). Both of these learning outcomes were measured using the same test instrument but differentiated in order item and order response option.
C. Methodology The objective of this study is to assess the effect of learning using multimedia towards exploratory learning outcomes in the subjects of Electrical Power Generation. The independent variable of this research is the instruction applying the Exploratory multimedia, and the dependent variable is the learning outcomes, namely Memory Recall (M.Rc) as measured by the Immediate Post Test (I.PT) and Memory Retention (M.Rt) as measured by Delayed Post Test (D .PT), while the moderator variables are Spatial Abilities were divided into two groups: High Spatial Ability (H.SA) and Low Spatial Ability (L.SA). The samples are 74 students divided into two classes of learning. Each class consists of H.SA and L.SA, given the same treatment by using exploratory multimedia. Two weeks before the treatments there have been carried out pre-test. The treatment is conducted for 5 week, every week is I.PT after learning process implemented, while D.PT performed two weeks after that. Studies using pre-test post-test experiment model [51]; [52], with 2 x 2 factorial design.
Spatial Ability
Learning with Exploratory Multimedia M.Rc M.Rt H.SA L.SA Figure 3. Design of research
The research instrument consisted of a multimedia feasibility test instruments, spatial ability test and achievement test. Multimedia exploratory built has eligibility test of alpha and beta with indicators which follow Alessi & Trollip theory [53]. Achievement test instrument was developed in parallel to measure the ability of pre-test and post-test (M.Rc/I.PT) and (M.Rt/D.PT). Pre test instruments, M.Rc/I.PT and M.Rt/D.Pt have the same content, level of difficulty and discrimination power, but distinguished order of items and response options. Test instrument developed by researchers and has been through the test validity and reliability, difficulty index and discriminatory power. Spatial ability was measured using instruments Purdue Spatial Visualization Test/TOR [46]. Analyzed using descriptive statistics and inferential
Universitas Ubudiyah Indonesia ISSN : 2442-
statistics is One way ANCOVA [54]; [55]. The test is performed at significance level α = 5%.
D. Results and Discussion The treatment is done during five weeks followed by 74 students who were divided into two classes of learning. The measurement of learning outcomes is presented in Table 1.
Table 1. Description of Learning Outcomes Pre Test I.PT (M.Rc) D.PT (M.Rt) N Valid 74 74 74 Mean 8.01 33.62 29. Median 8.00 34.00 29. Mode 8 39 34 Std. Deviation 3.13 5.89 6.
Table 1 shows that there is an increase learning achievement with a mean score of 25.61 on I.PT (M.Rc) compared Pre Test, while the D.PT (M.Rt) decreased the mean score of 3.74 out of I.PT (M.Rc). This proves that the use of multimedia exploratory learning can improve learning outcomes, increasing the mean score of 25.61 and after two weeks of retention test (D.PT) there was only a decrease in the mean score of 3. on the recall test (I.PT). To prove whether there are differences in learning outcomes Memory Recall (M.Rc) in the group of students H.SA and L.SA performed One-way ANCOVA. Levene's Test of Equality of Error Variances, F = .009; p = .923 has met the test requirements. Results of one-way ANCOVA test by controlling the Pre Test scores are shown in Table 2.
Table 2. Tests of Between-Subjects Effects Dependent Variable: Memory Recall (M.Rc/I.PT)
Source
Sum of Squares df Mean Square F Sig. Intercept 4106.64 1 4106.64 250.86. Pre Test 605.07 1 605.07 36.96. Spatial Ability 86.74 1 86.74 5.30. Total (^8) 6186.00 74
One-way ANCOVA results are shown in Table 2 (F = 5.30; p = .02) were significant differences in the mean scores M.Rc of H.SA and L.SA groups is significant. It gives the sense that there is a positive influence on spatial abilities M.Rc learning outcomes, and the effect is significant at the level α = 5%. It could be argued that spatial abilities affect learning outcomes
Universitas Ubudiyah Indonesia ISSN : 2442-
Table 5. Estimate of Mean Marginal for M.Rt, with Pre-test controlled Dependent Variable: Memory Retention (M.Rt/D.PT) Spatial Ability N Relative Mean Estimate with Pre-Test Score contolled Mean Mean Std.Error Lower Bound Upper Bound H.SA 32 34.06 31.94 .77 30.39 33. L.SA 42 26.69 28.31 .66 27.00 29.
The relative mean scores of groups H.SA 34.06 and 26.69 L.SA group. This proves that the group H.SA obtains higher learning achievement than L.SA group so that it can be concluded that spatial ability has a positive effect on learning outcomes. Furthermore, after controlling for pre-test scores, the mean score of the group and the mean score of H.SA group 31.94, and mean score of L.SA group 28.31. H.SA groups’ mean score decrease M.Rt 2.12 from the mean relative. On the other hand, L.SA group experienced a mean score increase M.Rt 1.62 of the relative mean. The relative mean sore 7.37 H.SA group has higher than the L.SA group due to Pre Test scores influence, whereas after the Pre Test scores are controlled such differences only 3.63. Statistical tests prove there is a significant difference between the groups of students learning outcomes H.SA and L.SA, both for memory recall (M.Rc) and memory retention (M.Rt). Details in detail the results of statistical tests are shown in Table 6.
Table 6. Summary of Learning Outcomes for Differencies Spatial Ability
Multimedi a Learning
Spatial Abil ity
Pre Test I.PT (M.Rc) D.PT (M.Rt)
Mean Diff Mean Diff**
Mean Diff*
Explorator y tutorial (ETLn)
Total 74 8.01 33.62 29. Differences (Pr.T- I.PT) :^ 25.^
Differences (I.PT- D.PT)
Table 6 shows that exploratory multimedia learning proves the significant effect of spatial ability on learning outcomes. Students in groups H.SA are higher than the student group L.SA. In the pre-test there is score difference 3.50; and on I.PT (M.Rc) there are differences in the mean scores 6.50; whereas in D.PT (M.Rt) the different mean score 6.40. This is in line with studies conducted [39]; [44]; [56]; [57]; [50]; [40]. In the immediate post-test (M.Rc) a mean score increased 25. from pre-test mean score. But the learning outcomes in delayed post-test
Universitas Ubudiyah Indonesia ISSN : 2442-
(M.Rt) after two weeks of the immediate post-test (M.Rc) decreased with a mean score of 3.74. The mean score differences are significant at α =. level. These findings coincide with [58]; [57]; [6]; [40].
E. Conclusions and Suggestions Exploratory learning using multimedia proven to improve students’ learning outcomes.There was an increase in the mean score of 25.61 on memory recall (M.Rc / I.PT) than the Pre Test. After a period of two weeks of retention the mean score decreased of 3.74 memory retention (M.Rt/D.PT) than (M.Rc/I.PT). This study also proves that spatial abilities has positive effect on learning outcomes on M.Rt and M.Rc. H.SA group proved a higher ability than L.SA group with the mean score difference 3.50 on the Pre Test, differ 6.50 in the Immediate Post Test (M.Rc) and differ 6.40 on Delayed Post Test (M.Rt). The effect of exploratory learning with multimedia is relatively gives greater influence to H.SA, but after controlling Pre test scores turn out a greater impact on the L.SA group.The engineering Instructional should use exploratory multimedia to improve learning outcomes
F. References
[1] Gilbert,J.K. (200 8 ). Visualisation : An emergent field of practice and inquiry science education. In John, K, Gilbert (Ed),. Visualization : Theory and Practice in Science Education ( 3 - 24 ). Netherlands: Springer
[2] ACCI (The Australian Chamber of Commerce and Industry). (2002). Employability Skills an Employer Perspective: Getting What Employers Want Out of The Too Hard Basket.
[3] Lauglo, J. (2005). Vocationalised secondary education revisited. In J. Lauglo & R. Maclean (Eds.), Vocationalisation of Secondary Education Revisited. Springer: Dordrecht. 3–49.
[4] Stevenson, J. C. (2003). Examining cognitive bases for differentiating technology education and vocational education. In G. Martin & H. Middleton (Eds.), Initiatives in Technology Education – Comparative Perspectives. Proceedings of the American Forum (194–206). Gold Coast, Australia: TFA and CTER
[5] Mustapha, Ramlee. (2014). TVET personnel professional development in the asia pacific: challenges and prospects. 3 rd^ UPI International Conference on Technical and Vocational Education and Training. Bandung (Indonesia), 12-15 November 2014.
[6] Pavlova, Margarita. (2009). Technology and vocational education for sustainable development, empowering individuals for the future. Queensland, Australia : International Centre for Technical and Vocational Education and Training. Springer.
[7] Abdullahi, S., & Ehsanyar, A.S. (2014). Relevance of current vocational curriculum in Afghanistan to market. 3rd UPI International Conference
Universitas Ubudiyah Indonesia ISSN : 2442-
Vocational Education and Training. Bandung (Indonesia), 12- November 2014.
[21] Grabe, Mark and Grabe, Cindy (2004). Integrating technology for meaningful learning (4th^ ed). Newyork : Houghton Mifflin Company
[22] Gagne, R.M., Briggs,L.J., & Wager, W.W. (1992). Principle of Instructional Design. New York : Harcourt Brace Jovaanovich College Publisher.
[23] Driscoll, M.P. (2005). Psychology of Learning for Instructio n. Boston : MA: Allyn & Bacon Publishers.
[24] Omrod, J.E. (2004). Educational Psychology. Upper Saddle River. New Jersey : Prentice Hall.
[25] Schunk, D.H. (2004). Learning Theories : An Educational Perspective , (4th). Upper Saddle, New Jersey : Merill Prentice-Hall.
[26] Miller,P.H.(1993). Theories of Developmental Psychology (3rd Ed).W.H.Freeman &Co.New York.
[27] Baddeley, A., Eysenck, M.W., & Anderson, M.C.(2009). Memory. New York : Psychology Press.
[28] Mayer, R.E. (2014). Multimedia Learning. (2nd) New York : Cambridge University Press.
[29] Paivio, Allan. (2006). Dual coding theory and education. Draft chapter for the conference on “Pathways to Literacy Achievement for High Poverty Children,” The University of Michigan School of Education, September 29-October 1, 2006.
[30] Merrienboer,J.J.G., & Kester,L. (2005). The four-component instructional design model : Multimedia priciples in environment for complex learning. In Mayer, R.E (ed), Multimedia Learning (71-96). New York : Cambridge University Press.
[31] Toh Seong Chong. (2005). Recence advances in cognitive load theory research : Implication for instructional designers. Malaysian Online Journal of Instructional Technology , 2 (3), 106-117.
[32] Clark, Ruth Colvin., & Mayer, R.E. (2008). e-Learning and the Science of Instruction, 2 nd Ed. San Francisco : John Wiley & Sons, Inc.
[33] Sweller, John, (2005). Implication of cognitive load theory for multimedia learning. In Mayer, R..E (ed). The Cambridge Handbook of Multimedia Learning (19-30). New York : Cambridge University Press.
[34] Paas, F, Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32 (1- 2),1-8.
[35] Sweller, John. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science , 32(1-2), 9-
Universitas Ubudiyah Indonesia ISSN : 2442-
[36] Ignacio, R. Madrid., & José J. Cañas. (2009). The effect of reading strategies and prior knowledge on cognitive load and learning with hypertext. The Ergonomics, Open Journal , (2). 124-132.
[37] Horton, William. (2000). Designing Web Based Training, John Wiley & Son Inc. USA.
[38] Thomas, Kent. (2004). Learning Sequences. New York : Rocky Mountain Alchemy.
[39] Melguizo,M.C.P., Vidya,Uty & Oostendorp, H. (2012) Seeking information online: the influence of menu type, navigation path complexity and spatial ability on information gathering tasks, Behaviour & Information Technology , 31(1), 59-70, DOI: 10.1080/0144929X.2011.
[40] Lu Wang & Martha Carr (2014) Working Memory and Strategy Use Contribute to Gender Differences in Spatial Ability, Educational Psychologist , 49:4, 261-282, DOI: 10.1080/00461520.2014.
[41] Hegarty, M., & Waller, D. (2004). A dissociation between mental rotation and perspective-taking spatial abilities. Intelligence , 32, 175– 191.doi:10.1016/j.intell. 2003.12.
[42] Romanas V. Krivickas (2007). Laboratory instruction in engineering education. Global Journal of Engineering Education, (11)2; 191-
[43] Dori, Y.J and Belcher, John (2005). Learning Electromagnetism with Visualization and Active Learning, in Gilbert, John.K (ed.), Visualization in Science Education , pp. 187 - 216
[44] Sanchez, A., & Wiley, J. (2007). Spatial abilities and learning complex science topics. Poster presented at the 29th Annual meeting of the Cognitive Science Society, Nashville, TN.
[45] Black, A.A. (2005). Spatial ability and earth science conceptual understanding. Journal of Geoscience Education , 53(4), 1-17.
[46] Bodner, G.M., & Guay, R.B. (1997). The Purdue Visualization of Rotation Test. The Chemical Educator , 2(4), 1-17.
[47] Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H. and Karthwohl, D.R. (1979). Taxonomy of Educational Objectives : The Classification of Educational Goals (new impression). London : Longman Group Limited.
[48] Betrancourt, M. (2014). The animation and interactivity principles in multimedia. In Mayer, R.E. (Ed). Multimedia Learning , (2nd), 134-141. New York: Cambridge University Press.
[49] Fletcher,J.D., & Tobias,S. (2014). The multimedia principle. In Mayer,R.E (ed). The Cambridge Handbook of Multimedia Learning , (117-134). Cambridge: Cambridge University Press.
[50] Haynie,W.J. (1997 ). Effect of anticipation of test on delayed retention learning_. Journal of Education_ , 9(1), 20-30.
[51] Gay,L.R.,Mills,E., & Airasian,P (2006). Educational Research. Upper Saddle ( th Eds). New Jersey : Pearson Merrill Prentice Hall.
Universitas Ubudiyah Indonesia ISSN : 2442-
Mutiawati, S.Pd., M.Pd
mutia@uui.ac.id
This study exploring teachers' difficulties students when solving problems and teachers’ effort to help students overcome these difficulties. Classroom practices and strategies teachers used in their attempts to foster student problem solving success were also studied. Data included analyses of interview transcriptions of students and teachers' responses. Findings from teachers' responses showed students' abilities to read and understand the problem was the most frequently cited difficulty; standardized testing and text difficulties were the most cited causes of those difficulties. Data analysis showed that students were not successful at obtaining solutions for the following reasons (1) Lack of comprehension of the problem posed, (2) Lack of strategy knowledge; (3) Inability to translate the problem into a mathematical form; (4) Inability to use the correct mathematics; (5) Inappropriate strategy used, (6) Incorrect formulation of the mathematical form; (7) Computational errors; (9) Imperfect mathematical knowledge; and (10) Misinterpretation of the problem.
Keywords: solving mathematicalproblems, difficulties, students , teacher
One such educator was a third-grade mathematics teacher who came and talked to the authors about the current state mandated mathematics test. During this conversation, she noted that the state’s third- grade mathematics assessment was becoming increasingly a test of a student’s ability to read and understand the problems instead of a test of computation skills (Daniel, Faye, Kimberly and Claricia, 2013). Edwards, Maloy, and Anderson (2009) identified seven specific word- and comprehension-related challenges students faced when taking the test. Edwards and colleagues made suggestions for each of the seven areas and issued a call for others to examine state tests and identify word and comprehension challenges. Jordan, Kaplan, and Hanich (in Daniel, Faye, Kimberly and Claricia, 2013), which found in a two-year longitudinal study that reading disabilities predicted children’s progress in mathematics; however mathematics disabilities did not affect children’s progress in reading.
Universitas Ubudiyah Indonesia ISSN : 2442-
Current interest in children’s mathematics skills has led to Singapore’s Ministry of Education continued to revise the syllabus for the mathematics curriculum in schools with a primary aim to enable students to develop their abilities in mathematical problem solving (Ministry of Education, 2006). Also of international concern, a results of the Fourth National Assessment of Educational Progress (NAEP) of mathematics which showed that students performed poorly on solving word problems (Kouba, Brow, Carenter, Lindquist, Silver, & Swaford, 1988). Similarly, the results of further analysis of TIMSS states that students grades 7 and 8 did not perform well for items that required them to solve non-routine problems (Kaur & Yap, 1999). This study further aims to review the factors that affect both thecognitive, affective student, teachers and mathematics educators should focus on the possible difficulties faced by the students as they interact with the mathematical problem and problem solution. In view of this, the purpose of this paper is to exploring what teachers say about student difficulties solving mathematical and teachers’ effort to help students overcome these difficulties when students solving problems.
LITERATUR REVIEW Lester and Kehle (2003, p. 510) typify problem solving as an activity that involves the students‟ engagement in a variety of cognitive actions including accessing and using previous knowledge and experience. Where as Mayer (1982, 1987) explained problem-solving processes as using different forms of knowledge leading to the goal of solving the problem. According to him, the types of knowledge applied in problem solving consisted of: linguistic and factual knowledge – about how to encode statements schema knowledge – about relations among problem types algorithmic knowledge – about how to present distinct procedures, and strategic knowledge – about how to approach problems In fact, according to Newman (1983), difficulty in problem solving may occur at one of the following phases, namely reading, comprehension, strategy know-how, transformation, process skill and solution. Schoenfeld (1985) suggested four aspects that contributed to problem-solving performance. These are the problem solver‟s: (1) mathematical knowledge, (2) knowledge of heuristics, (3) affective factors which affect the way the problem solver views problem solving, and (4) managerial skills connected with selecting and carrying out appropriate strategies. In their study of the problem-solving research literature, Kroll and Miller (1993) identified three major cognitive and affective factors; namely, knowledge, control (metacognition) and beliefs and affects that contributed to students difficulties in problem solving. Further Lester (1994) expressed that difficulties experienced during problem solving could also be caused by the problem solvers characteristics such as: traits – such as spatial visualization ability and ability to attend to the structural features of problems,