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EE-5101-F INTRODUCTORY EDUCATION RESEARCH

EE-5101-F INTRODUCTORY EDUCATION RESEARCH. research. Group F HJH NURHAFIZAH IZZATI HJ MARALI (11M8067) SYAZWINA BINTI HJ MAHMOD (11M8068) MAS HANI BINTI MURAH (11M8144) DIDINAWATI BINTI HJ ZUNAIDI (11M8131) SAFIAH BINTI HJ YAKUP (11M8129). Outlines:. Titles of articles :.

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EE-5101-F INTRODUCTORY EDUCATION RESEARCH

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  1. EE-5101-F INTRODUCTORY EDUCATION RESEARCH research Group F HJH NURHAFIZAH IZZATI HJ MARALI (11M8067) SYAZWINA BINTI HJ MAHMOD (11M8068) MAS HANI BINTI MURAH (11M8144) DIDINAWATI BINTI HJ ZUNAIDI (11M8131) SAFIAH BINTI HJ YAKUP (11M8129)

  2. Outlines:

  3. Titles of articles:

  4. Titles of articles:

  5. Purpose of the studIES

  6. Purpose of the studIES

  7. methodology

  8. participants • - 5 boys and 2 girls between the of aged 8 – 10 years old. • - 5 year old boy • - 11 members of IEP team

  9. Data analysis • Case Study 1 • Emicperspective: • No participants’ view were recorded or found in the article. • Etic perspective: • It is the author/researcher’s opinion (from the summary of findings) that the TI-83 Plus graphing calculator can be used for both numerical and graphical procedures. • Graphing calculator helps to ensure that a student’s mathematical ability is used to the fullest.

  10. Data analysis • Case Study 2 • Only test results of students who took both the pre-test and post-test were used for analysis. • The results were collected by looking at: • The mean scores of the pre-test and post-test, • The normalized gain to see the effectiveness of the intervention, • The Repeated Measures ANOVA to see if the FMCE performance of the two groups are significantly different.

  11. Mean Scores. Pre-test Post-test

  12. Results from Preliminary Investigation Results from the study

  13. Average Normalized Gain. • Introduced by Hake (1998) as a way to analyse the effectiveness of a teaching method measured by diagnostic tests such as FCI or FMCE. • It is the ratio of an actual average gain and a maximum possible average gain.

  14. <g> Ranges. 0 - 0.3 : Low Gain 0.3 - 0.7 : Medium Gain 0.7 - 1.0 : High Gain

  15. Average Normalized Gain.

  16. Normalized change. • Proposed by Marks and Cummings (2007) to solve the problem of getting negative gain.

  17. Control Group (TI)

  18. Treatment Group (PISI)

  19. Repeated Measures ANOVA. • Use to compare the change running from pre-test to post-test.

  20. Data analysis • Case Study 3 • Among the three interview subject, Allison was the only child who solved all 8 interview questions successfully. • She displayed the characteristics of a ‘visualize’ or ‘geometric ‘type showing the tendency to visualize the problem situation. • The interview with Allison revealed salient features in her thinking processes while solving mathematical word problems.

  21. Data analysis • Characteristics trait is visualizing the problem situation: • She does so in her head only for questions that she has encountered in a similar context before. • For questions that are new to her, she will visualize through drawing diagrams (detailed or sketchy) on paper to help her think through the problem context. • She is able to make the connection between a previously solved problem and transfer the previous solution in the current problem situation.

  22. Data analysis • Characteristics trait is visualizing the problem situation: • The interviewer noticed that Allison always wrote down mathematical statements after already obtaining the solution from the diagram. • Bishop (1989) cautioned that a child’s preference for solving problems, whether using visual methods or non-visual ones, may be influenced by the classroom teacher’s problem solving style. • Here, Allison learns to conform to her mathematics teacher’s expectation that all solutions should be accompanied by mathematical statements.

  23. Data analysis • Case Study 4 • Routine process logs were taken and analysed in order to check the correlation between specific activities, interactions, elements in nature and the group and individual processes. • Data were then analysed and categorised in order to explore the meaning within the overall context of the work. • Established principles were used in order to form and support the construction of theory (McLeod, 2002, 2003). • After the data were analysed, using Reason’s collective inquiry principles (McLeod, 2002; Reason, 1994), a draft paper was sent to the group facilitators for their reactions which were then integrated in the writing of this article.

  24. Data analysis • Case Study 5 • The field notes reflect thin threads of tentative hypotheses that may represent either emerging patterns or snags that may require further examination. • It shows that it is an emic approach because it allows participants and data to 'speak' to the researcher and allow new patterns to emerge.

  25. Similarities and differences

  26. Outlines:

  27. CASE STUDY • DEFINITION: • Yin (1984): Case study research is an empirical inquiry that investigates a contemporary phenomenon within real-life context. • Gillham (2000): A case study is one which investigates on individual or a community.

  28. TYPES OF CASE STUDY

  29. Exploratory case studies set to explore any phenomenon in the data which serve as a point of interest to the researchers.

  30. Descriptive case studies is to describe the natural phenomena which occur within the data in question.

  31. Explanatory case studies examine the data closely both at a surface and deep level in order to explain the phenomena in the data.

  32. How to conduct CASE STUDY?

  33. STRENGTHS OF CASE STUDY

  34. LIMITATIONS OF CASE STUDY

  35. References • Berger, R. (2006). Using contact with nature. Creativity and rituals as a therapeutic medium with children with learning difficulties: a case study. Emotional and Behavioural Difficulties. Vol. 1, 2, 135-146. • DechaSuppapittayaporn, NarumonEmarat, and Kwan Arayathanitkul (2010). The Effectiveness of Peer Instruction and Structured Inquiry on Conceptual Understanding of Force and Motion: A Case Study in Thailand. Research in Science and Technological Education. Vol.28 (1), 63-79. • Gillham, B. (2000). Case Study Research Methods. London: PastonPrePress Ltd. • Ho Siew Yin (2007). Allison: A Case Study of Spatial Visualization in Mathematical Problem Solving. EARCOME4, UniversitiSains Malaysia. • Jeyaletchumi (2007).Graphing Calculators and Assessment : A Case Study. EARCOME4, UniversitiSains Malaysia. • PalenaNaela, ShyamThapa. Preparing A case study: A Guide for Designing and conducting a case study for Evaluation Input. Retrieved on March 11, 2012 from http://www.pathfind.org/site/DocServer/m_e_tool_series_case_study.pdf • Ruppar, A.L. & Gaffney, J.S. (2011). Individualized Education program Team Decisions: A Preliminary Study of Conversations, Negotiations, and Power. Research & Practice for Persons with Severe Disabilities. Vol.36 (1-2), 11-22. • ZaidahZainal (2007). Case Study as A Research Method. JurnalKemanusian. Vol. 9, 1-5.

  36. THANK YOU FOR LISTENING

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