Introduction
How is that the questions are generated before the literature review? The gaps in the lit that generates qns.
The purpose of the study is to understand the students profile in terms of their goal orientation (task, ego and work-avoidance). The study is also used to ascertain whether there are correlations between their problem-solving achievement and their profile. The design and implementation of the Science Problem Solving Checklist (SPSC) is to ascertain if students' problem-solving achievement and metacognition skills will improve.
Therefore, we embarked on the project to answer the following research questions.
How do we align the constructs in metacognition to customise for a localised SPSC?
What is the goal-orientation profile of the students in the study?
Is there a correlation between students' goal-orientation profile and gender to the students' problem-solving achievement?
Does the students' problem-solving skill improve with the use of the SPSC?
Does the students' metacognition skill improve with the use of the SPSC?
Some of the above are closed qns
Reframe the qns. From your powerpoint, your responses do not relate to the above qns. I suggest you reframe your qns.
Literature Reviews
In reading the various research articles, we seek to understand the following:-
What does research say about the learning of science and the strategies to enhance students' mastery of scientific problem-solving?
What kinds of pedagogical content knowledge (PCK) are used by science teachers?
What is the nature of the problem-solving process?
What are the differences in cognitive and behaviours of novice and experts in science problem-solving?
What are some of the assessment tools in checking pupils' mastery of science problem-solving?
What is the relationship among metacognition, self-regulation and the problem-solving process in enhancing student achievement?
What strategies are recommened in the learning of physics?
(which set of qns will be used for this study? I am confused.
Learning of science and strategies to enhance students' scientific conceptual mastery
Effective Problem-Solving Strategies
Research has shown that students should learn effective problem solving strategies in order to develop expertise in physics. Specifically, they must be able to solve problems beyond those that can be solved using a plug-and-chug approach (Maloney, 1994). Converting a physics problem from an initial verbal representation to other suitable representations such as diagrammatic, tabular, graphical, or algebraic can make further analysis of the problem easier (Larkin, 1985). Similarly, using analogies and considering limiting cases are also useful strategies for solving problems (Gick & Holyoak, 1987). Many traditional physics lessons do not explicitly teach students effective problem solving heuristics and might implicitly reward inferior problem solving strategies used by many students.
Mason and Singh (2010) shared that teachers may not explicitly discuss and model these strategies while solving problems in class. Recitation is usually taught by teachers who present homework solutions on the blackboard while students copy them in their notebooks. Without proper guidance, most textbook problems do not help students monitor their learning, reflect upon the problem solving process, and pay attention to their knowledge structure.
Heller et al. (1992) showed that group problem solving is especially valuable for learning physics and for developing effective problem solving strategies.
The researchers have developed many context-rich problems that are close to everyday situations and are more challenging and stimulating than standard textbook problems. These problems require careful thought and the use of many problem representations. Working with peers in heterogeneous groups with high, medium, and low performing students is particularly beneficial for learning from context-rich problems, and students are typically assigned the rotating roles of manager, time keeper, and skeptic by the instructor.
Quantitative and conceptual problem solving both can enhance problem solving and reasoning skills only if students engage in effective problem solving strategies instead of treating the task purely as a mathematical chore or guess-work. The abstract nature of the laws of physics and the chain of reasoning required to draw meaningful inferences make it even more important to teach students effective problem solving strategies explicitly. (Singh, 2009)
Reflection Exercise
Reflection is an integral component of effective problem solving (Black and William, 1998). There are diverse strategies that can be employed to help students reflect upon problem solving. One useful approach is "self-explanation" or explaining what one is learning explicitly to oneself (Chi, et al., 1989). Chi et al. found that while reading science texts, students who constantly explained to themselves what they were reading and made an effort to connect the material to their prior knowledge performed better on problem solving on related topics given to them after the reading.
Writing -To-Learn in Science
There is a widespread current advocacy of the value of using writing-to-learn in secondary science (Champagne & Kouba, 1990; Hand et al., 1999; Kelly & Chen, 1999). Hand, et al. (1999) postulates that writing-to-learn strategy enhances pupils' conceptual knowledge, develops scientific literacy, and familiarizes pupils with the expectations, conventions and reasoning skills. The use of journals in the science classroom has been proposed as a possible method for improving the problem solving skills of pupils, for monitoring pupils' thinking and understanding, and for enhancing pupils' learning (Fulwiler, 1987; Malachowski, 1988). MacVaugh (1990) found that using journals was effective with most pupils for enhanced learning in different content areas.
Table 1summarises the learning strategies for science. It can be inferred from the literatures that problem-solving strategies such as visualisation techniques, reflection exercises and writing activities are important for the students to attain. However, in those literatures, there is no clear distinction between the effectiveness of each strategy on different learning profile, age or gender specific groups. Therefore, it is crucial to read from other literatures on the learning profile, age and gender specific to address those said areas.
Learning strategies
What did research say?
Author (Year)
Problem-solving
(using analogies, being explicit, using heterogenous cooperative groups etc.
solve problems beyond using plug-and-clung approach (what does that mean?)
convert problem from verbal representation to suitable representations (tables, graphs or algebra) makes analysis easier
using analogies and considering limiting
group problem solving is valuable for learning physics and for developing effective problem solving strategies
working with peers in heterogeneous groups with high, medium, and low performing students is particularly beneficial for learning from context-rich problems
importance of teachers in providing explicit explanations and discussion as well as modeling relevant strategies while solving problems in class
Larkin (1985)
Maloney (1994)
Gick & Holyoak (1987)
Heller et al. (1992)
Heller et al. (1992)
Mason and Singh (2010);
Singh (2009); Chi, et al. (1989)
Reflection Exercises
students who constantly explained to themselves what they were reading and made an effort to connect the material to their prior knowledge performed better on problem solving on related topics given to them after the reading
Chi, et al. (1989)
Writing -To-Learn in Science
enhances pupils' conceptual knowledge, develops scientific literacy, familiarizes pupils with the expectations, conventions and reasoning skills
use of journals in the science classroom has been proposed as a possible method for improving the problem solving skills of pupils, for monitoring pupils' thinking and understanding, and for enhancing pupils' learning
using journals was effective with most pupils for enhanced learning in different content areas
Hand, et al. (1999)
Fulwiler (1987), Malachowski (1988).
MacVaugh (1990)
Table 1: Learning Strategies for Science
Pedagogical Content Knowledge (PCK) of Science Teachers
Shulman (1987) describes pedagogical content knowledge (PCK) as the knowledge of subject matter for teaching. It includes knowledge of students' difficulties and prior conceptions in the domain, knowledge of domain representations and instructional strategies, and knowledge of domain specific assessment methods (refer to Fig 1.)
Van Driel (1998) shared that in a number of studies, teaching practice was investigated as a function of familiarity with a specific domain. These studies lead to similar results, indicating that teachers, when teaching unfamiliar topics, have little knowledge of potential student problems and specific preconceptions, and have difficulties selecting appropriate representations of subject matter. Moreover, when teaching unfamiliar topics, teachers express more misconceptions (Hashweh, 1987) and they talk longer and more often, and mainly pose questions of low cognitive level (Carlsen, 1993). Sanders et al. (1993) stated that experienced science teachers, when teaching a topic out of their area of certification, seem to be sustained by their wealth of general pedagogical knowledge, while their PCK is limited. The authors also noticed that experienced teachers quickly learn the new content as well as adequate content specific instructional strategies, while relying on their knowledge of general pedagogy. The latter helps them to maintain the flow in their classes. The authors concluded that pedagogical knowledge provides a framework for teaching that is "filled in by content knowledge and pedagogical content knowledge . . . when teachers taught within and outside their science area" (Sanders et al., 1993)
Several international studies on PCK have also been conducted (Adams & Krockover,1997) which have identified the components of PCK. These components include: 1) knowledge and beliefs of teaching objectives, 2) knowledge and beliefs of the science curriculum, 3) knowledge and beliefs of teaching strategies, 4) knowledge and beliefs of students' understanding of science, and 5) knowledge and beliefs of learning assessment. In two partially overlapping studies, Clermont et al. (1993, 1994) investigated chemistry teachers' PCK with respect to chemical demonstrations as an instructional strategy. The second study compared PCK of experienced and novice demonstrators, concluding that experienced teachers possess a greater repertoire of representations and strategies when demonstrating a particular topic. Moreover, they are able to use certain demonstrations more flexibly for various purposes, and they can relate their demonstrations more effectively to student learning than novices. In the first study, the effects on PCK of an in-service workshop for novice demonstrators were investigated. As growth of novices' PCK toward that of experienced demonstrators was observed the authors concluded that PCK "can be enhanced through intensive, short-term, skills-oriented workshops" (Clermont et al., 1993, p. 41).
Nature of Problem-Solving
Research has shown multiple interpretations of what is a problem-solving process. Newell & Simon in 1972 described it as a decision making process that occurs when a solver is presented with a task for which they have no specific set of actions they can use to research a solution. Hayes (1989) defined the problem solving process as a statement as follows "whenever there is a gap between where you are now and where you want to and you do not know how to find a way to cross the gap, you have a problem.
Solving a problem means finding an appropriate way to cross the gap." One of the early modern attempts to identify stages involved in the type of quantitative problem solving used in mathematics and science was by mathematician Pólya (1945). In his first step "Understanding the Problem", the solver summarizes known and unknown information, introduces suitable notation, and draws a figure. Next in "Devising a Plan", the solver uses their knowledge to plan how to connect the given data to the desired goal. Then in "Carrying out the Plan", the solver implements their plan by carrying out the necessary procedures to reach an answer while checking their work along the way. The final step is "Looking Back" or examining the result to check that it makes sense, and if possible using an alternative procedure to achieve the answer. In the aspect of chemistry problem-solving where the goal is specified, the student needs to be familiar with the fundamental principles and concepts and be able to recall easily this to solve the problem. The retrieval of information is facilitated by the storage of "chunks" of related ideas in the memory. One purpose of the "Look Back" step suggested in the problem-solving strategy is to help to identify related chemical principles and concepts (Woods, 1987).
Difference in Cognitive and Behaviours of Novice and Experts in Science Problem-Solving
Information about problem solving processes and knowledge structures have been obtained from research studies comparing experienced or "expert" problem solvers to inexperienced or "novice" problem solvers (Docktor et al., 2007). Many of these studies focused on the content of physics knowledge and its mental organisation as a basis for explaining observed process differences. Research has also shown the when compared to experts, novices tend to have an incoherent knowledge structure. (Larkin et al., 1980a), employ means-ends analysis and have difficulty transferring their knowledge to other contexts.
Experts generally begin solving problems with a conceptual analysis and have a much organised knowledge structure, categorise physics problems by fundamental principles and can transfer their knowledge when confronted with a new situation. Experts also engage in a low-detail overview of problem features and expectations, called qualitative analysis, before writing down quantitative relationships (Feltovich et al., 1981; Larkin et al., 1980a). Experts use this information to consider possible solution approaches or physics principles that might be useful in solving the problem. Novices tend to skip this step and jump directly to writing down the equations (Reif & Heller, 1982).
Experts also have strong mathematical skills and strategies for monitoring progress and evaluating their answers (Larkin et al., 1980a; Reif & Heller, 1982). Larkin (1979) and Feltovich et al. (1981) had drawn conclusions about the content and mental organisation of physics knowledge. They found that an expert's memory is structured hierarchically around a small number of fundamental physical principles called "chucks". Such principles are considered as fundamental because they can be applied to a wide range of physical situations (Larkin, 1981). In contrast, the novice's knowledge structures are clearly disconnected and there is no clear link between the physics principles and application procedures. Table 2shows the comparison between novice and expert problem-solving processes.
Expert
Novice
very organised knowledge structure
incoherent knowledge structure
categorise physics problems by fundamental principles
employ means-ends analysis
can transfer their knowledge when confronted with a new situation
difficulty transferring their knowledge to other contexts
engage in a low-detail overview of problem features and expectations, called qualitative analysis, before writing down quantitative relationships
knowledge structures are clearly disconnected
memory is structured hierarchically around a small number of fundamental physical principles called "chucks"
no clear link between the physics principles and application procedures
Table 2: Comparison between Expert and Novice's Problem-Solving Processes
The Assessment Tools in checking Pupils' Problem-Solving Mastery
A form of assessment tool (Docktor et al., 2007) was developed at the University of Minnesota in the form of a rubric, which subdivides the problem solving process into five approximately independent aspects and assigns a separate score for performance in each category. These categories are: useful description, physics approach, specific application of physics, mathematical procedures and logical progression.
Useful Description assesses a problem solver's process of organising information from the problem statement into an appropriate and useful representation that summarises essential information symbolically, visually and/or in writing. A problem description could include specifying known and unknown information, assigning appropriate symbols for quantities, stating a goal, a sketch or picture of the physical situation.
Physics Approach assesses a solver's process of selecting appropriate physics concepts and principles to use in solving the problem. Here the term "concept" is used to mean a general physics idea, such as the general concept of vector or specific concepts such as momentum and velocity. The term "principle" is used to mean a fundamental physics rule or law used to describe objects and their interactions, such as conservation of energy or Newton's third law. The Physics Approach category reflects the expert-like process of selecting relevant physics principles before applying them to the specific context of the problem (Larkin et al., 1980b).
Specific Application of Physics assesses the solver's process of applying physics concepts and principles to the specific conditions in the problem. Specific application often involves connecting the objects and quantities in the problem to the appropriate terms in specific physics relationships. It can include a statement of definitions, relationships between quantities, initial conditions, and consideration of assumptions or constraints in the problem. Writing down specific physics relationships, typically in the form of equations, can be seen as another aspect of planning the solution (Heller et al., 1992; Reif et al., 1976). This category is similar to the problem-solving model by Larkin et al. (1980b) that designates "connecting symbols in an equation with information in the problem" as a process that follows "selecting relevant physics principles" and "generating the corresponding equation".
Mathematical Procedures assesses the solver's process of executing the solution with respect to selecting appropriate mathematical procedures and following mathematical rules to obtain target quantities. Examples of these procedures include: isolate and reduce strategies from algebra, substitution, use of the quadratic formula, matrix operations, or "guess and check" from differential equations. The term mathematical "rules" refers to processes from mathematics, such as the Chain Rule in calculus or appropriate use of parentheses, square roots, logarithms, and trigonometric identities.
Logical Progression assesses the solver's processes of communicating reasoning, staying focused toward a goal, and evaluating the solution for consistency. The category checks whether the overall problem solution is clear, focused, and organized logically. Several models of problem solving emphasize the final stage as looking back (Pólya, 1945) or evaluating the solution to check that it makes sense (Reif et al., 1976; Van Heuvelen, 1991). However, steps such as planning and evaluation or checking the result could help a student avoid errors in consistency and coherence, which are scored as part of the logical progression.
Checklists by other Researchers in Science Problem-Solving
Holman (2001) suggested the use of problem-solving checklist to help students develop met cognitive skills as they develop and evaluate their thinking processes. The topic of Chemical Calculation as suggested by research demands students to derive the solution to the problem using a series of sequential steps and a checklist is found to be effective in helping them to gain greater confidence and competency in solving problems (Hinton & Nakhleh, 1999).
Metacognition
The Metacognition Awareness Inventory (Schraw & Dennison, 1994) is a 52-item questionnaire specifically developed to assess people's self-understanding or awareness of their metacognitive processes. It reflects peoples' awareness of themselves in a non-specific learning context and it assesses awareness of general learning capabilities and strategies. It measures two major components of metacognition: (1) knowledge about one's own Cognition; and (2) regulation of one's own Cognition (Schraw & Dennison, 1994, see also Schraw, 1998).
Knowledge about Cognition refers to what people know about themselves as learners, self-understanding of their strong and weak points, strategies, and the conditions under which strategies are most effective (assessed using such items as: "I understand my intellectual strengths and weaknesses"; "I have a specific purpose for each strategy I use"; "I am aware of what strategies I use when I study").
Regulation of Cognition reflects peoples' perceptions about the ways they plan and implement strategies, monitor and correct comprehension errors, and evaluate their learning (e.g., "I ask myself questions about the material before I begin"; "I am a good judge of how well I understand something"; "I know how well I did once I finish a test").
Three processes of metacognitive regulation are typically posited (Schraw, 1995): (1) Planning, which refers to the selection of appropriate strategies and allocation of cognitive resources before the task; (2) Monitoring, which refers to the awareness of understanding and performance during the task; and (3) Evaluation, which refers to the appraisal of performance after task completion. In previous studies, these two domains of metacognition were strongly interrelated, indicating that knowledge and regulation work together to assist in self-regulation.
Self-regulation in Learning
Self-regulation in Learning (SRL) refers to the self-directive process through which learners transform their mental abilities into task-related academic skills (Zimmerman, 2001). Students are self-regulated to the degree that they are metacognitive, motivationally, and behaviorally active participants in their learning (Zimmerman, 1989). They are generally characterized as active, efficiently managing their own learning through monitoring and strategy use (Green & Azevedo, 2007).
Most theoretical models of SRL include the following key components: (a) a goal, a standard, or a criterion by which individuals assess their current performance, thus guiding their regulation processes (Locke & Latham, 1990; Pintrich, 2000); (b) tactics and strategies that are applied to achieving the goal and are chosen after the goal components and goal requirements are analyzed (Butler, 1998); (c) a multilevel process of monitoring and control in light of a perceived feedback and awareness to one's behavior (Mace, Belfiore, & Shea, 1989), including perceiving external and/or internal cues, establishing internal feedback and acting accordingly; and (d) self-efficacy, which is regarded both as related to the motivational aspect of SRL (Bandura, 1993) and as one of the personal traits related to self-regulation (Matthews, Schwean, Campbell, Saklofske, & Mohamed, 2000).
Studies have documented the important contribution of SRL to academic success (e.g., Bandura, 1997; Pintrich & DeGroot, 1990; Schunk & Zimmerman, 1994). Thus, self-regulated learners exhibit higher levels of involvement, effort, and consistency while performing academic tasks than do their low SRL peers, with student achievements consistently demonstrated to be meaningfully related to their self-reported SRL (Corno, 1986; Zimmerman & Martinez-Pons, 1988).
In SRL, setting goals is one of the important components. Goals are drives that guide behavior, cognition and affect as students engage in academic work. The literatures on achievement motivation clearly identify a number of different goals. Earlier works of Dweck & Elliot (1985) identified both task and ego goals, with task goals focusing on mastery of learning and ego goals stressing competition and comparison, emphasizing performance. Recent works by Elliot (1997), McGregor and Elliot (2002) and Pintrich (2000), the performance goal has been revised to draw a distinction between approach performance ego-orientation (outperforming others) and an work-avoidance performance orientation (avoidance of looking incompetent).
Hau & Hui (1996) reported that work avoidance (getting work done with minimum effort) was positively related to rote and reproductive learning strategies. Wolters, Yu and Pintrich (1996) demonstrated that higher levels of approach performance goal orientations were positively related to the use of cognitive and metacognitive strategies.
Physics learning style and gender differences
Severiens and Ten Dam (1994) postulates that males show a greater preference than females for the abstract conceptualisation mode of learning. Girls preferred to learn physics in a conversational style and collaborative activity, and work with concrete objects. Boys, on the contrary, liked to learn through argument and individual activity, and tended to use more abstract thinking. Most classroom activities were organised to accommodate male learning styles (Ong, 1981).
Strategies to motivate different genders in the learning of physics
Kitchenham (2002) postulates that there should be alternation between group discussion and structured teaching. He mentions that females perform better when they are able to articulate their thoughts verbally and males perform better when their learning experience is structured.
Studies carried out by Laws et. al (1999) and Schneider (2001) suggest that females benefit especially by the use of active pedagogies. Kimbell et. al (1991) say that females tend to learn more when they express ideas in words through discussion, whereas males prefer working independently.
Lorenzo et. al (2006) shared their findings that providing interactive engagement effectively reduces the gender gap in physics performance. Females improve their performance most and overcome a considerable pre-instruction gender disparity.
Design
Conceptual Framework
The diagram below shows the conceptual framework of the design of the study. The independent variables are gender, goals and science self-concept. The intervention tool is the Science Problem-Solving Checklist (SPSC) and the dependent variables are the achievement test scores and the metacognition ratings in the survey questionnaire. The study was a quasi-experiment two groups pretest-posttest design with the pupils from intact classes, without randomization.
Gender
Goals
(TG, E, WAG)
Intervention
(SPSC)
Achievement Scores
Metacognition
Science Self-Concept (SC)
Subject Characteristics
A total of 80 medium/low ability and mixed gender pupils from two intact classes of 15 years old from the Normal (Academic) stream participated in this study. They have an average Primary School Leaving Examination Score between 155 to 180. The racial mix is 60% Chinese, 35% Malay and 5% Indian.
Teacher Profile
The teacher involved in the study is a female teacher with 6 years of teaching experience. She is trained in teaching both physics and mathematics.
Instruments
SPSC Operational Definition for Metacognition
Planning (P) is goal setting and allocating resources prior to learning
Information Management (IM) is skills and strategy sequences used on-line to process information more efficiently. Examples are organizing, elaborating, summarizing, selective focusing.
Monitoring (M) is the assessment of one's learning or strategy used.
Survey Questionnaire Scales
The 36-items were partly obtained from the modified version of the 52-items called Metacognition Awareness Inventory for adults created by Schraw and Dennison (1994). Factor Analysis has show a reliability (α = .90) and interrelated (r = .54) for the 52-item. The other 36-items were obtained from the modified Personal Goals Scale (adapted from Nicholls, Patashnick & Nolen, 1985) was used to assess students' task, ego and work avoidance orientations in school situations. The original scale consists of 24 items grouped into three sub-scales: Student Task Goals (TG), Student Ego Goals (EG), and Student Work Avoidance Goals (WAG). The other scale is obtained from the Science Self-Concept (SC). The students and Table 3 shows the summary of which the scales are obtained and their reliability.
Items
Scale
Sources
1 to 17
Metacognition (M)
Schraw and Dennsion (1994)
reliability (α = .90) and
interrelated (r = .54)
18 to 22
Task Goal (TG)
Ee (1998) as adapted from Nicholls, Patashnick & Nolen (1985)
reliability (α = .846)
23 to 27
Ego Goals (EG)
Ee (1998) as adapted from Nicholls, Patashnick & Nolen (1985)
reliability (α = .926)
28 to 32
Work Avoidance Goals (WAG)
Ee (1998) as adapted from Nicholls, Patashnick & Nolen (1985)
reliability (α = .817)
33 to 36
Science Self-Concept (SC)
Unknown source
Table 3: Survey Questionnaire Scales
Factor Analysis
The original scale items were modified into 36-items administered and students were asked to rate each item on a four-point Likert scale. The students respond from "Strongly Agree" (SA) to "Strongly Disagree" (SD). The rating scale is SA for 1 to SD for 4.The surveyed ratings was averaged across the items with each scale. In order to determine the validity and reliability of the instrument. Table 4 shows the reliability of each item using the principal component analysis, rotation method; Promax with Kaiser Normalization. The reliability range of .79 to .87 shows that the scales used are highly reliable. However, there are individual items which can be highly reliable. They are selected item 2, 3, 4, 8, 9, 10, 11, 12, 14, 15, 17 from the M Scale and all the items in the TG, EG, WAG and SC scales.
Items
Item reliability
Scale reliability
Scale Reliability
1
.522
Metacognition (M)
.84
2
.636*
3
.669*
4
.716*
5
.569
6
.567
7
.432
8
.646*
9
.798*
10
.601*
11
.707*
12
.727*
13
.442
14
.672*
15
.726*
16
.573
17
.727*
18
.640
Task Goal (TG)
.82
19
.732
20
.737
21
.775
22
.717
23
.629
Ego Goal (EG)
.79
24
.731
25
.584
26
.832
27
.816
28
.798
Work Avoidance Goal
(WAG)
.87
29
.865
30
.607
31
.868
32
.836
33
.840
Science Self-Concept
(SC)
.86
34
.832
35
.887
36
.644
Table 4: Reliability coefficients for each item
Measure
There is an administration of the 36-items questionnaire (Annex A) for pretest and the posttest after 2 weeks of intervention. The intervention tool is the use of science problem-solving checklist (SPSC) in Annex B. The pretest achievement score is based on the common test administered two weeks before the study. The common test consists of a mixture of problem-solving questions on the physics topic of Kinematics and Physical Quantities. The total marks for the pretest achievement test is 20. The posttest achievement score is obtained from the test on the physics topic of Dynamics which is 8 marks to only assess on relevant problem-solving portions in the topic.
Procedure
The two classes namely Sec 3A1 (control group) and Sec 3A2 students (experimental group) were both taught by the same teacher for the entire duration of the study. The pretest achievement was obtained before the study. The pretest questionnaire is administered on the first week of the study. On the 2nd week, the Sec 3A2 students (experimental group) were introduced to the use of SPSC for topic of Dynamics and started using SPSC as a reference to solve problems. Two weeks after the use of the SPSC, the pupils from Sec 3A1 and 3A2 did the posttest achievement test and questionnaire.
Results and Analysis
The data analysis involved the use of t-test for both descriptive and inferential statistics. The correlation matrix is also used to ascertain the pretest, posttest and goal scales, and the pretest and posttest achievement test scores. The central research question which served as the focus of this study was answered quantitatively through statistical analysis using SPSS. The mean, standard deviation, and median of the pretest and posttest scores in the achievement scores and survey will be compared between the experimental and control groups.
Research Question: What is the goal-orientation profile of the students in the study?
Profile
Mean
Standard Deviation
Meta
1.92
0.39
TG
1.72
0.42
EGO
1.68
0.49
WAG
2.95
0.70
SC
2.25
0.74
Table 5: Students' Profile with Mean and Standard Deviation
It can be observed that the students tend to have a higher ego goals (1.68) followed by task goals (1.72), and work-avoidance (2.95) goals. The science self-concept (2.25) of the students tends to be slightly less desirable.
Research question: Is there a correlation between students' goal-orientation profile and gender to the students' problem-solving achievement?
Gender
posttest
M
TG
EG
WAG
SC
Gender
1.00
posttest
*-0.16
1.00
M
-0.23
-0.13
1.00
TG
-0.04
-0.15
*0.60
1.00
EG
*-0.38
0.03
0.09
0.08
1.00
WAG
0.01
0.13
*-0.39
*-0.49
-0.03
1.00
SC
*-0.34
*-0.26
*0.32
0.28
0.27
-0.05
1.00
Table 6: Correlations between gender, posttest, M, TG, EG, WAG and SC
The correlation test between gender and posttest score shows a -0.16 value. The weak negative correlations (-0.26) between the posttest and the SC shows that negative SC will result in better performance. The female students tend to do better slightly better in the posttest scores. Gender is weakly negatively correlated to the EG (-0.38) goals and SC (-0.34). As for the TG and WAG, there is a negatively correlation between them (-0.39). As for M, the strongest correlation is to TG (0.60). It means that students who have positive metacognition skills also have positive task goals. The close negative relations between TG and WAG (-0.49) shows that work-avoidance goal is negatively related to task goal. There is no significant correlations between posttest scores to the gender, M, TG, EG, WAG and SC.
Research Question: Does the students' problem-solving skills improve with the use of the SPSC?
The achievement results for pretest and posttest is as follows. It can be observed that 3A2 is a weaker ability group than 3A1 based on the pretest scores. In the pretest, the score gap between the girls is 6.5 and in the posttest, the score gap between the girls is reduced to 0.6. As for the boys, the pretest score difference was 6.3 and the posttest score gap was instead increased to 20.9. The female students' score did not improve but the gap between the pretest and posttest score is also not significant. As for the boys, they did worse for the posttest scores as the gap is greater.
3A1
3A2
Mean
60.6
54.1
SD
9.2
14.7
Table 7.1 Pretest achievement score comparisons for female students
3A1
3A2
Mean
59.7
59.1
SD
12.9
30.3
Table 7.2 Posttest achievement score comparisons for female students
3A1
3A2
Mean
64.1
57.8
SD
12.8
15.0
Table 7.3 Pretest achievement score comparisons for male students
3A1
3A2
Mean
64.9
44.1
SD
30.5
24.5
Table 7.3 Posttest achievement score comparisons for male students
Research Question: Does the students' metacognition skill improve with the use of the SPSC?
As we can observe from Table 8.1 and 8.2 there is no significant improvement in the metacognition scale for the experimental group as well as the control group.
pretest
posttest
Cont
Expt
Cont
Expt
Metacognition
Task Goal
Ego Goal
Work Avoidance Goal
Achievement
Discussion and Implications
The results show indicate that the students self-report themselves as highly metacognitive (1.92), task (1.72) and ego (1.68). This could be attributed to them being an a balanced of both female and male students. Research by Meese et. al (1988) and Wolters et. al (1996) has shown the students who adopted task goals reported greater and positive cognitive and metacognitive strategies. Therefore, the results obtained tally with what research has discovered. As for the WAG (2.95), this particular group of students might be unwilling to put in extra effort and therefore engage in work-avoidance goals to reach their objective. As for the lower Science Self-Concept, it can also be attitributed to them scoring below average scores, pretest (Common Test) < 65%). Therefore, their self-concept towards the learning of science is a case for concern.
In the correlation matrix, there is a clear relationship between the metacognition to task goals and therefore, those with those character traits should obtain better posttest achievement scores. However, the results did not give such a trend as predicted. The posttest results tend to be not significantly correlated to the learning's profile.
There could be an overlooking validity and reliability of the measuring instrument, which is the posttest. The questions asked could be tougher compared to usual way of asking questions in the topic. The topic is the toughest among the rest of the physics topic.
The manner in which the checklist was taught to the students could also be a contributing factor. During the teaching of the checklist, the teacher-centred approach was used. After the profiling of the students was done, it was discovered in research in Laws et. al (1999), Schneider (2001) and Kimbell et. al (1991) that the girls might need a more interactive or social interactions classroom learning setting to learn to apply the checklist. There could also be not enough iteration of the use of checklist to solve different physics questions. The rush to finish explaining the checklist could be detrimental to the learning style of the students. Therefore, there was no significant improve in the achievement score.
The posttest results for the female students tend to be slightly improved (smaller gap) as compared to the boys. The reason could be due to the girls displaying a greater level of the need to learn in a more interactive environment Lorenzo (2006) shared that females students improve their performance most with the implementation of interactive engagement activities. Also girls tend to be more tasks oriented as reported by many teachers in secondary schools.
The metacognition scale also did not improve after the SPSC intervention. The period of intervention (2 weeks) could be too short to reinforce their metacognition skills, which have been moulded at an earlier age. For future research, there has to be a longer intervention period for the acceptance of the strategy to enhance their metacognition skills.
Further Research
The need to standardise the pretest and postest is important to control the measuring instruments. As for the checklist, the current group was a Normal (Academic) group. Perhaps the use of the checklist can be implemented on the female students of different academic stream. The metacognition and achievement results obtained can predict of the checklist could be more suitable for the female students as they are more task oriented and has the level of maturity to cope with the new learning materials. The next cycle of research could be more of using the checklist on female students (experimental group) and not on male students (control groups) and to check their overall results.
The extraneous variable of learning environment could also be structured to take into account the preferred learning environment of each gender. The girls could learn to apply the checklist in a more interactive and social environment, which gives them the opportunity to articulate their learning.
The checklist could also be used in small bit sizes. The checklist needs to be age-appropriate and to suit the developmental stages of the students. Perhaps the use of the checklist can be implemented in phases. The students would be able to apply and internalise the logical sequencing to have a greater effectiveness in the mastery of the problem-solving and metacognition skills.
The phrasing of some the items in the survey also needs to be refined. The factor analysis of the survey instrument has released so items which should not be asked. Therefore, with so many survey items (36), the students could have suffered from survey fatigue and did not answer the questions truthfully.
STUDENT QUESTIONNAIRE
Class Index No.
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Name of Participant: ______________________________ Class: _____________
DIRECTIONS:
Please FILL IN THE BOX to indicate your opinion about each questionnaire statement.
There are altogether 36 statements for you to indicate.
1 - if you STRONGLY AGREE with the statement;
2 - if you AGREE with the statement;
3 - if you DISAGREE with the statement;
4 - if you STRONGLY DISAGREE with the statement; Box
I pace myself while learning in order to have enough time.
I set specific goals before I begin a task.
I ask myself questions about the material before I begin.
I read instructions carefully before I begin a task.
I organize my time to best accomplish my goals.
I slow down when I encounter important information.
I consciously focus my attention on important information.
I create my own examples to make information more meaningful.
I draw pictures or diagrams to help me understand while learning.
I ask myself what I already know when I am learning something new.
I find myself pausing regularly to check my understanding.
I ask myself every now and then if I am meeting my goals.
I know how well I did once I finish a test.
I summarize what I've learned after I finish.
I check my work to make sure I did it correctly.
I know what I have learned after each lesson.
I ask myself if I have learned as much as I could.
I am always trying to do better in my school work.
When I am improving in my school work I try even harder.
I work hard to try to understand new things at school.
The harder the problem, the harder I try at school.
I am always trying to do better my schoolwork
I would be happy if I did my work better than other people.
I hope to obtain first position in my class.
I'm happy to be the only one who can answer the teachers' questions.
I want to get higher marks than other pupils.
I want to get one of the highest marks in class.
I hope I can get away with not doing homework.
I want to bluff and get away with any work.
I would be happy if I didn't have to do any homework.
I want to fool around and get away with my work.
I try to do almost no work and get away with it.
I have always done well in SCIENCE.
I get good marks in SCIENCE.
SCIENCE is one of my best subjects.
Work in SCIENCE classes is easy for me.
End of questionnaire
Science Problem-Solving Checklist (SPSC)
Planning (P)
â-¡Reviewed the topic before attempting to solve the question
â-¡Recognise the simplest method to solve the problem
â-¡Recognise the physics topic (s)
â-¡Recall the correct formula (e)
Information Management (IM)
â-¡Underline the quantities with units
â-¡Place the relevant symbol of the quantity above the number (ie. Put m above mass 20 kg; constant speed a = 0 ms-2; F =0 N, horizontal force 80 N)
â-¡<In case of a diagram given>, label the diagram with the relevant keys words (e.g. 80N ïƒ ; Ff friction ; F= 0 N ïƒ )
â-¡<In case of a diagram not given>, sketch the diagram and label the diagram with the relevant keys words
â-¡ Underline the key question requirement (e.g. calculate, state, explain or describe)
â-¡ Observe the marks awarded for the question & answer accordingly to the marks awarded (e.g. 1 mark for working shown & 1 mark for answer and unit)
Monitoring (M)
â-¡Check the step-by-step working(s) as the answer is being worked out
â-¡Reveal of errors as the step-by-step working(s) are checked
Evaluation (E)
â-¡Applied the simplest way to solve the problem
â-¡Ensure that the explanations/workings/answer correspond to the marks allocated
