mcm mana nak interpret t test spss output

As salam...ni pon topik basi ni..tp nak masuk jugak kat sini..sbb bila lama tinggal..mampuih lupa semua..setiap kali sambung blaja, setiap kali repeat balik mende ni..lupa sbb x amalkan..yelah...kita buka guna spss nak beli barang kat tesco kan..hahahahahah...

Ckp pasal t test kan..kompom2 la statistik kan..selalunya org xde masalah nak dptkan result guna spss..pastu..nak interpret...menggagau x reti..spss tu bagi result dlm bentuk nombor..kalau x reti baca maksud nombor tu..xde maknanya...

basicnya t test ada 3 jenis..semua org slalunya dah familiar kan..simple t test..independent t test dan sekor lagi..dependent  test a.k.a paired t test...

sblm syok2 ber t test..pastikan data korg2 ni semuanya normally distributed eh..bell atau gaussian shape..bukan skewed ke kiri atau kanan..graph bentuk m ke v ke..bukan ek..itu kene dipastikan..how ler?? kau buat la NORMALITY TEST guna spss tu haa..... tp kalau tgk dari bacaan varians..pon dah boleh agak juga nanti data kita jenis mcm mana..normal atau not normally distributed...mende varians tu??

Varians tu ialah satu bacaan yang menunjukkan mcm mana data tu disperse/bertabur/berselerak daripada min..eh...sebelum kau kompius..min tu purata..benda yg sama

makanya, kalau nilai varians kau besar..maknnaya lagi jauhla tersebar data2 tu daripada min..so..xde maknalah min itu..maksudnya lagi..sampel kau tu tidak homogenus..bila x homogenus maknanya not normally distributed..so xleh la pakai t test..sbb test ni parametrik, apply utk data normal saje..data tidak normal..silalah pi buat non parametrik test..ngerti??

t test ni guna utk tengok mean difference (perbezaan min)..jadi basicnya ko kene buat descriptive stats dulu..maknanya kene tau berapa mean, mod, varians semua2 tu..yg basic2..sbb ada kalau melalui descriptive stats..sah2 nampak ada mean different yg sagt besar dan jelas..x payah la gedik nak continue dgn t test..t test ni inferential stats..

cth output spss

kau tekan2 spss..keluar table ni..acaner nak baca??
1. ko tgk figure pada sig colume..tu levene test punya reading..sbb nilai levene test tu 0.582 (p>0.05), lebih besar dr significant 0.05..maka varians ada sama...maknanya kita akan amil bacaan equal variance (row atas) utk tgk t test value pulak..
2. kat ni t test value dia 0.14 (p>0.05), kita accept null hypotesis dengan rumusan bahawa tiada perbezaan min antara.....ko sambung ayat tu ikut variable kajian masing2 ye....

kalau mcm ni pulak???

sama aje la...
1. pi tgk kat Levene test bah sig tu..apsal?? sbb figure ni akan tentukan kita akan amik bacaan t test dr row atas atau bawah. Dalam kes ni, nilai sig Levene test ialah p=0.006 (p>0.05)..so..bacaan kita mesti dari row pertama.
2. bacaan t test..nampak 0.000..sah2 la p<0.005, so kita reject null hypothesis..dengan rumusan bahawa wujud perbezaan min antara.....ko sambung ayat tu ikut variable korg.....
perbezaan ini bermakna tau.. meaning...

cth: lelaki skor 50, perempuan skor 54
berapa beza?? 4 je kan...tp bila buat t test,,wujud perbezaan yg signifikan sbbP<0.05, so bermakna wujud perbezaan yg signifikan walau nilai kecil

senang kan!!
dah..kbye!

Theories of Problem Solving

** teori problem solving dtg dr 2 ni je..yg lain2 tu model..paham takkkkk..jgn kompius lagi

** sbb research focus ialah PS strategy..kita pilih Newell & Simon (1972) hoccay..ingat tuu

Many current views of problem solving, such as described in Keith Holyoak and Robert Morrison'sCambridge Handbook of Thinking and Reasoning (2005) or Marsha Lovett's 2002 review of research on problem solving, have their roots in Gestalt theory or information processing theory.

Gestalt Theory. The Gestalt theory of problem solving, described by Karl Duncker (1945) and Max Wertheimer (1959), holds that problem solving occurs with a flash of insight. Richard Mayer (1995) noted that insight occurs when a problem solver moves from a state of not knowing how to solve a problem to knowing how to solve a problem. During insight, problem solvers devise a way of representing the problem that enables solution. Gestalt psychologists offered several ways of conceptualizing what happens during insight: insight involves building a schema in which all the parts fit together, insight involves suddenly reorganizing the visual information so it fits together to solve the problem, insight involves restating a problem's givens or problem goal in a new way that makes the problem easier to solve, insight involves removing mental blocks, and insight involves finding a problem analog (i.e., a similar problem that the problem solver already knows how to solve). Gestalt theory informs educational programs aimed at teaching students how to represent problems.

Information Processing Theory. The information processing theory of problem solving, as described by Allen Newell and Herbert Simon (1972), is based on a humancomputer metaphor in which problem solving involves carrying out a series of mental computations on mental representations. The key components in the theory are as follows: the idea that a problem can be represented as a problem space—a representation of the initial state, goal state, and all possible intervening states—and search heu-ristics—a strategy for moving through the problem space from one state of the problem to the next. The problem begins in the given state, the problem solver applies an operator that generates a new state, and so on until the goal state is reached. For example, a common search heuristic is means-ends analysis, in which the problem solver seeks to apply an operator that will satisfy the problem-solver's current goal; if there is a constraint that blocks the application of the operator, then a goal is set to remove the constraint, and so on. Information processing theory informs educational programs aimed at teaching strategies for solving problems.

source:http://www.education.com/reference/article/problem-solving1/

IBL vs PBL vs CBL

We see IBL as a pedagogy which best enables students to experience the processes of knowledge creation. The core ingredients of an IBL approach that most researchers are in agreement with are:

• learning is stimulated by inquiry, i.e. driven by questions or problems;
• learning is based on a process of constructing knowledge and new understanding;
• it is an 'active' approach to learning, involving learning by doing;
• a student-centred approach to teaching in which the role of the teacher is to act as a facilitator;
• a move to self-directed learning with students taking increasing responsibility for their learning; and
• the development of skills in self-reflection.

Several modes of IBL are discussed in the literature. One framing we find useful is that of Staver and Bay (1987) who distinguish between structured, guided and open inquiry. Their definitions were particularly oriented towards problem solving, but we broaden their categories to allow exploration of issues. Thus we distinguish between:

• structured inquiry – where teachers provide an issue or problem and an outline for addressing it
• guided inquiry – where teachers provide questions to stimulate inquiry but students are self-directed in terms of exploring these questions
• open inquiry – where students formulate the questions themselves as well as going through the full inquiry cycle as given in Figure 1.

*CBL (guided inquiry) over PBL (open inquiry)

The relationship between IBL, problem-based learning (PBL), and case-based learning (CBL) is less clear. Problem-based learning has a well developed literature base but like IBL, the definition of the term is contested and again there are a variety of approaches that fall under the umbrella term of PBL. All approaches may begin with a question, although open inquiry often starts with a general theme or issue from which students develop a particular question to be addressed. The timescale for IBL (over weeks or months) is typically much longer than for either PBL (hours to weeks) or CBL (minutes to hours). Whilst open inquiry promotes student choice in terms of the topic of learning, in PBL and CBL, the content and skills to be learned are usually far more prescribed. CBL and PBL are thus akin to structured and guided forms of IBL. In all approaches the teacher’s role is one of a facilitator. Given these relations between the three approaches, the research team decided that PBL was a more prescriptive form of IBL, and CBL a more focussed form of PBL, giving a nested hierarchy within the realm of active learning (Figure 2).

Figure 2: Proposed relation between inquiry-based 

learning (IBL), problem-based learning (PBL) and case-based learning (CBL). IBL, PBL and CBL fall in the realm of active learning; PBL is a subset of IBL and CBL is a subset of PBL (adapted from Spronken-Smith et al., 2008).

Herreid (2007) provides 11 basic rules for case-based learning.

1. Tells an engaging Tstory.
2. Focuses on an interest-arousing or controversial issue
3. Set in the past five years
4. Creates empathy with the central characters.
5. Includes quotations. There is no better way to understand a situation and to gain empathy for the characters
6. Relevant to the reader.
7. Must have pedagogic utility.
8. Conflict provoking.= Requires the reader/viewer to use information in the case to address the problem
9. Decision forcing.= Requires the reader/viewer to think critically and analytically to address the problem
10. Has generality.
11. Is short, Brevity – has just enough information for a good analysis

Why Use Case-Based Learning?

To provide students with a relevant opportunity to see theory in practice. Real world or authentic contexts expose students to viewpoints from multiple sources and see why people may want different outcomes. Students can also see how a decision will impact different participants, both positively and negatively.

To require students to analyze data in order to reach a conclusion. Since many assignments are open-ended, students can practice choosing appropriate analytic techniques as well. Instructors who use case-based learning say that their students are more engaged, interested, and involved in the class.

To develop analytic, communicative and collaborative skills along with content knowledge. In their effort to find solutions and reach decisions through discussion, students sort out factual data, apply analytic tools, articulate issues, reflect on their relevant experiences, and draw conclusions they can relate to new situations. In the process, they acquire substantive knowledge and develop analytic, collaborative, and communication skills.

Many faculty also use case studies in their curriculum to teach content, connect students with real life data, or provide opportunities for students to put themselves in the decision maker's shoes.

Research has shown that case-based learning has been very successful at providing a context for abstract material. Cases also provide an ‘experience’ for students that can be transformed into learning through reflection or experimentation. Case-based learning has been linked with the effective development of critical thinking, problem solving, clinical reasoning and analysis, which in turn are characteristics of a deep approach to learning. It also can be used to facilitate a model of self-directed and reflective learning that serves students very well in future courses and careers. (Dunne and Brooks, 2004).

Designing Case Study Questions

Cases can be more or less “directed” by the kinds of questions asked—these kinds of questions can be appended to any case, or could be a handout for participants unfamiliar with case studies on how to approach one.

• What is the situation—what do you actually know about it from reading the case? (Distinguishes between fact and assumptions under critical understanding)
• What issues are at stake? (Opportunity for linking to theoretical readings)
• What questions do you have—what information do you still need? Where/how could you find it?
• What problem(s) need to be solved? (Opportunity to discuss communication versus conflict, gaps between assumptions, sides of the argument)
• What are all the possible options? What are the pros/cons of each option?
• What are the underlying assumptions for [person X] in the case—where do you see them?
• What criteria should you use when choosing an option? What does that mean about your assumptions?

Managing Discussion and Debate Effectively

• Delay the problem-solving part until the rest of the discussion has had time to develop. Start with expository questions to clarify the facts, then move to analysis, and finally to evaluation, judgment, and recommendations.
• Shift points of view: “Now that we’ve seen it from [W’s] standpoint, what’s happening here from [Y’s] standpoint?” What evidence would support Y’s position? What are the dynamics between the two positions?
• Shift levels of abstraction: if the answer to the question above is “It’s just a bad situation for her,” quotations help: When [Y] says “_____,” what are her assumptions? Or seek more concrete explanations: Why does she hold this point of view?”
• Ask for benefits/disadvantages of a position; for all sides.
• Shift time frame—not just to “What’s next?” but also to “How could this situation have been different?” What could have been done earlier to head off this conflict and turn it into a productive conversation? Is it too late to fix this? What are possible leverage points for a more productive discussion? What good can come of the existing situation?
• Shift to another context: We see how a person who thinks X would see the situation. How would a person who thinks Y see it? We see what happened in the Johannesburg news, how could this be handled in [your town/province]? How might [insert person, organization] address this problem?
• Follow-up questions: “What do you mean by ___?” Or, “Could you clarify what you said about ___?” (even if it was a pretty clear statement—this gives students time for thinking, developing different views, and exploration in more depth). Or “How would you square that observation with what [name of person] pointed out?”
• Point out and acknowledge differences in discussion—“that’s an interesting difference from what Sam just said, Sarah. Let’s look at where the differences lie.” (let sides clarify their points before moving on).

Source:

• http://www.queensu.ca/ctl/what-we-do/teaching-and-assessment-strategies/case-based-learning
• http://www.usask.ca/gmcte/case-based
• Herreid, C. F. (2007). Start with a story: The case study method of teaching college science. NSTA Press.
• http://pt.slideshare.net/Learning_Instruction/casebasedlearning/4

Problem-Based Learning: What and How Do Students Learn? (Hmelo-Silver, 2004)

Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn?Educational Psychology Review, 16(3), 235-266.

Souce: http://kanagawa.lti.cs.cmu.edu/olcts09/sites/default/files/Hmelo-Silver_2004.pdf

1.  Problem-based learning (PBL) is an instructional method in which students learn through facilitated problem solving ~~ Psychological research and theory suggests that by having students learn through the experience of solving problems, they can learn both content and thinking strategies.
2. work in collaborative groups -- Collaborative problem-solving groups are a key feature of PBL
3. engage in self-directed learning (SDL) and then apply their new knowledge to the problem and reflect on what they learned and the effectiveness of the strategies employed
4. teacher acts to facilitate the learning process rather than to provide knowledge

GOALS OF PBL 

Problem-based curricula provide students with guided experience in learning through solving complex, real-world problems. PBL was designed with several important goals (Barrows and Kelson, 1995). It is designed to help students 

1) construct an extensive and flexible knowledge base; 

2) develop effective problem-solving skills; One indicator of effective problem-solving skills is the ability to transfer reasoning strategies to new problems.

3) develop self-directed, lifelong learning skills; 

4) become effective collaborators; and 

5) become intrinsically motivated to learn..

PBL is one of a family of approaches that include anchored instruction and project-based science.

~~ all three approaches use a common problem and rely on the teacher to help guide the learning process. They differ in terms of the type and role of the problem, the problem-solving process, and the specific tools that are employed

There are at least two key issues that go to the heart of all of these approaches to learning through problem solving. 

• First, all the approaches emphasize that learners are actively constructing knowledge in collaborative groups. 
• Second, the roles of the student and teacher are transformed. The teacher is no longer considered the main repository of knowledge; she is the facilitator of collaborative learning. The teacher helps guide the learning process through open-ended questioning designed to get students to make their thinking visible and to keep all the students involved in the group process. In anchored instruction and project-based science, the teacher does some direct instruction, often when students need information for the problem-solving activities.The SDL emphasis is a distinguishing feature of PBL. In PBL, students become responsible for their own learning, which necessitates reflective, critical thinking about what is being learned (Bereiter and Scardamalia, 1989). In PBL, students are asked to put their knowledge to use and to be reflective and self-directed learners.

The Role of the Problem

• To foster flexible thinking, problems need to be complex, ill-structured, and open-ended; to support intrinsic motivation, they must also be realistic and resonate with the students’ experiences. A good problem affords feedback that allows students to evaluate the effectiveness of their knowledge, reasoning, and learning strategies. 
• The problems should also promote conjecture and argumentation. Problem solutions should be complex enough to require many interrelated pieces and should motivate the students’ need to know and learn. As students generate hypotheses and defend them to others in their group, they publicly articulate their current state of understanding, enhancing knowledge construction and setting the stage for future learning (Koschmann et al., 1994).
• Good problems often require multidisciplinary solutions.
• Good problems also foster communication skills as students present their plans to the rest of their class. Multidisciplinary problems should help build extensive and flexible knowledge because information is not learned in isolation.

The Role of the Facilitator

• In PBL, the teacher/facilitator is an expert learner, able to model good strategies for learning and thinking, rather than an expert in the content itself. The facilitator scaffolds student learning through modeling and coaching, primarily through the use of questioning strategies
• Facilitators progressively fade their scaffolding as students become more experienced with PBL until finally the learners adopt many of the facilitators’ roles ~~ Although the facilitator fades some of his or her scaffolding as the group gains experience with the PBL method, s/he continues to monitor the group, making moment-to-moment decisions about how best to facilitate the PBL process. 
• The facilitator is responsible both for moving the students through the various stages of PBL and for monitoring the group process. This monitoring assures that all students are involved and encourages them both to externalize their own thinking and to comment on each other’s thinking (Hmelo-Silver, 2002; Koschmann et al., 1994). 
• The PBL facilitator (a) guides the development of higher order thinking skills by encouraging students to justify their thinking and (b) externalizes self-reflection by directing appropriate questions to individuals. The facilitator plays an important role in modeling the problem solving and SDL skills needed for self-assessing one’s reasoning and understanding. The facilitator directly supports several of the goals of PBL. First, s/he models the problem solving and SDL processes. Second, the facilitator helps students learn to collaborate well. An underlying assumption is that when facilitators support the learning and collaboration processes, students are better able to construct flexible knowledge.

**The role of the facilitator is extremely important in modeling thinking skills and providing metacognitive scaffolding. 

Teaching & Learning Approach/ Strategy/ Technique/ Method

As Salam..

Note utk diri sendiri..mohon jangan kompius...ya ampunnnn

Source:

1. http://www.slideshare.net/justindoliente/principles-of-teaching-33070911
2. http://www.slideshare.net/switlu/different-approaches-and-methods?username=diyana7@live.utm.my
3. http://www.slideshare.net/jaezek1804/selection-use-of-teaching-strategies-different-approaches

Designers Should Enhance Students’ Ill-Structured Problem-Solving Skills

 Identifying Questions to Investigate

Designers Should Enhance Students’ Ill-Structured Problem-Solving Skills

Namsoo Shin & Steven McGee
Copyright © 2003.
http://www.cotf.edu/vdc/entries/ILLPS.html

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What are ill-structured problems?

Ill-structured problems are ones students face routinely in everyday life. They include important social, political, economic, and scientific problems (Simon, 1973). In order to resemble situations in the real world, ill-structured problems have unclear goals and incomplete information (Voss, 1988).
Students who develop robust solutions for ill-structured problems usually engage in the following processes: a) define the problem, b) generate possible solutions, c) evaluate the alternative solutions by constructing arguments and articulating personal beliefs, d) implement the most viable solution, and e) monitor the implementation (Jonassen, 1997; Shin, Jonassen, & McGee, 2003; Sinnott, 1989).

Why is ill-structured problem solving important?

• Enhance cognitive skills.
  Well-developed domain knowledge is a primary factor in solving ill-structured problems (Jonassen, 1997; Roberts, 1991). In solving ill-structured problems, students apply their domain knowledge in a meaningful way instead of storing a chunk of concepts in a memory (White & Frederiksen, 1998).

• Enhance metacognitive skills.
  Ill-structured problems require solvers to control and regulate the selection and execution of a solution process (Brown, Bransford, Ferrara, & Campione, 1983; Flavell, 1987; Gick, 1986; Jonassen, 1997; Jacobs & Paris, 1987). In the ill-structured problem-solving processes, students employ their metacognitive skills, such as change strategies, then modify plans and reevaluate goals in order to reach a optimal solution (White & Frederiksen, 1998).

• Enhance argumentation skills.
  Since ill-structured problems require students to consider alternative solutions, successful students providing evidence for their solution (Voss, 1988; Voss & Post, 1988; Jonassen, 1997). Therefore, students gain practice justifying their solution in a logical way to persuade others.

How does a designer create ill-structured problems?

• Design a complicated problem that we face in everyday life.
  Ill-structured problems should come from a real-life situation in which there is no obvious right answer. Problems should be authentic and relevant to students (Howard, McGee, Shin, & Shia, 2001). Ill-structured problems should include vaguely defined goals. The information available to the decision maker should be incomplete or ambiguous (Wood, 1993). Problems should make it unclear which concepts, rules, and principles are necessary for the solution.

• Design a problem including multiple solutions and perspectives. 
  Ill-structured problems must allow alternative solutions instead of one correct answer (Meacham & Emont, 1989). Additionally, ill-structured problems should allow students to pursue different procedures for solving the problem. These various procedures will come from allowing different perspectives based on students’ perceptions and interpretations of the nature of the problem. 

References
Brown, A. L., Bransford, J., Ferrara, R., & Campione, J. (1983). Learning, remembering, and understanding. In P.H. Musen (Ed.), Handbook of child psychology: Vol. III (pp. 77-166). New York: Wiley.

Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. Weinert & U.R. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 21-29). Hillsdale, NJ: Erlbaum.

Gick, M. L. (1986). Problem-solving strategies. Educational Psychologist, 21, 99-120.

Howard, B., McGee, S., Shin, N, & Shia, R (2001). The triarchic theory of intelligence and computer-based inquiry learning. Educational Technology Research and Development, 49(4), 49-69.

Jacobs, J. E., & Paris, S. G. (1987). Children’s metacognition about reading: Issues in definition, measurement, and instruction. Educational Psychologist, 22, 255-278.

Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology: Research and Development, 45(1), 65-94.

Meacham, J. A., & Emont, N. M. (1989). The interpersonal basis of everyday problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and applications (pp. 7-23). New York: Praeger.
Roberts, D. A. (1991). What counts as an explanation for a science teaching event? Teaching Education, 3, 69-87.

Shin, N., Jonassen, H. D., & McGee, S. (2003). Predictors of well-structured and ill-structured problem solving in an astronomy simulation. Journal of Research in Science Teaching, 40(1), 6-33.

Simon, H. A. (1973). The structured of ill-structured problem. Artificial Intelligence, 4, 1981-201.

Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving: Theory and applications (pp. 72-99). New York: Praeger.

Voss, J. F. (1988). Problem solving and reasoning in ill-structured domains. In C. Antaki (Ed.), Analyzing everyday explanation: A casebook of methods (pp. 74-93). London: SAGE Publications.
Voss, J. F., & Post, T. A. (1988). On the solving of ill-structured problems. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.) The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum.

White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3-18.

Wood, P. K. (1993). Inquiring systems and problem structures: Implications for cognitive developments. Human Developments, 26, 249-265.