Metacognition plays an important role in communication, reading comprehension, language acquisition, social cognition, attention, self-control, memory, self-instruction, writing, problemsolving, and personality development. Metacognition includes knowledge and regulation of one's thinking processes. Metacognition is a special type of knowledge and ability that develops with personal experience and with schooling. Metacognition refers to a level of thinking that involves active control over the process of thinking that is used in learning situations. Planning the way to approach a learning task, monitoring comprehension, and evaluating the progress towards the completion of a task: these are skills that are metacognitive in their nature. In the present study, Metacognitive ability is the knowledge concerning one's own cognitive process and product. Metacognition literally means knowing about knowing or thinking about thinking. It is an appreciation of what one already knows, together with a correct comprehension of the learning task. Metacognition enables us to become successful learners and it has been associated with intelligence. Activities such as planning how to approach a given learning task, monitoring, comprehension and evaluating progress towards a completion of task are metacognitive in nature. Metacognition plays an important role during each level of problemsolving ability. Rezvan, Ahmadi and Abedi (2007) concluded that training in metacognition have positive effects on the academic achievement and increases students’ happiness. The findings of the studies also revealed that there is a positive and moderate relationship between the metacognitive reading strategies and scholastic achievement in science. It is also revealed from the findings that female students are better in metacognitive strategies than male students (Khan and Khan,2013).
problemsolving and realizing when they make errors (carlson, 1997; Glaser & chi, 1988). engaging in metacognitive activities, problem solvers become aware of their strengths, but also of their limitations (bransford, brown, & cooking, 1999) and suppressing metacognitive processes during problemsolving can lead to a decrease in performance (bartl & dörner, 1998). despite the multitude of studies about the role of metacognition in learning and teaching, several gaps are apparent. first, while studies on the effects of metacognitive instructional methods in reading and mathematics are relatively extensive, not much work has been done in everyday problemsolving. second, most of the investigations and interventions about the effects of metacognition in learning and teaching science have concentrated on secondary schools, colleges, and universities, but studies in elementary schools are rare. third, no research has been done about the contribution of metacognitive instructional intervention in the schools of developing countries, characterized by large classes, limited resources, hence content-based teaching and learning. We argue that children’s everyday problems require metacognition because such problemsolving situations are highly variable and success criteria depend on how the learner clarifies and reconciles competing solutions. according to Jonassen’s typology of problems (2004, 2007), there are 11 kinds of problems that vary according to their structuredness, complexity, and dynamicity. one of the problem types is decision-making, which is an everyday part of children’s lives (Jonassen, 2000). children make decisions in many situations including and not limited to, time allocation (whether to do homework or to play), what to buy, and social situations (how to facilitate friendships). they do so by considering the advantages and disadvantages of alternative solutions
Metacognition is described by Davidson et al., (1994) as an important process that contributes to problemsolving performance. Metacognition helps problem solvers to identify and define the problems; mentally represent the problems; plan how to proceed; evaluate what one knows about one’s performance (Davidson et al., 1994). Sternberg (1998) listed 12 characteristics of experts and 5 of them as below are characters of metacognition:
Flavell ([Fla76]) defined metacognitive as the knowledge of a person involved in the process of his or her own perception and results or anything related to them. Metacognition refers to the monitoring of activities and the regulation of results and coordination of processes related to cognitive objects, usually in the services of a specific object; According to Thorpe and Satterly ([TS90]) metacognition is often described multidimensionally and is used as a general term for a range of different levels of cognitive skills; According to ([BS15]) argued that metacognition is one of the possibilities for knowing what you know and what you do not know. It is also the ability to use your knowledge to plan a strategy for giving information, taking the necessary steps in problemsolving and to reflect a person's level of thought about a special concern; Martinez ([Mar06]) stated that metacognition is defined as teaching a person's brain to control thought processes in order to control their learning. Knowing that thinking can be controlled and directed, students will be aware of their own thinking power, then they become purposeful learners.
High-order thinking or Higher Order Thinking Skill (HOTS) is one of the most needed thinking skills in a person's life. Creative thinking is the highest level of thinking skills. Creative thinking skills in students have the characteristics of fluency (fluency), flexibility (flexibility), elaboration and originality (originality). Metacognition is the ability of one's thinking in using strategies to produce problemsolving in their learning. This ability helps students get personal feedback about their learning progress. From the research result, the students with high metacognition ability are at the level of creative thinking skill 4 (very creative), the student has been able to find the different pattern in the number pattern with the partition technique. Students with metacognition skills are at the creative skill level 3 (creative). While students with low metacognition abilities are at the level of creative thinking skills 2 (simply creative). Her creative thinking skill to 15 students can show his skill on fluency aspect, on flexibility aspect 11 students, while 5 students' originality aspect and elaboration aspects of 14 students. So the percentage of students with creative skill is on 100% fluency aspect, 73.3% flexibility aspect, 33,3% originality aspect and elaboration aspect 93,3%.
A study on metacognitive skills in relation to problem-solving in physics among secondary school students in Johor, Malaysia is sponsored by Research Management Centre (RMC), UTM under Vot 75161. It has finally completed its data collection and a complete Technical Report is due to be published in January 2007.The study involved a survey on 1300 physics students from nine districts of Johor namely Batu Pahat, Muar, Kota Tinggi, Pontian, Johor Bahru, Segamat, Mersing, Kulai and Kluang. Two well- validated instruments on metacognitive skills and four problem-solving questions on mechanics (Fatin, 2005) were conducted among respondents selected from 9 rural schools and 15 urban schools in Johor using random cluster samplings of form four physics students. The sampling of respondents in this study did not include students from fully residential schools where the “cream” of the Malay students are mostly found. The samples comprised students from daily secondary schools (rural and urban) and premier schools (urban High Schools) in Johor. This paper forms part of the report of the short term research project and focuses only on the development of gender and ethnicity differences on metacognitive and problem-solving skills from three stages of research processes. An indepth literature review related to metacognition and physics problem- solving is discussed fully in Fatin (2005).
Recent research has revealed the significance of metacognitive awareness in learning. For instance, learners who score high on measures of metacognition are more strategic , more likely to use problem-solving heuristics , better at predicting their test scores , and generally outperform learners who score low on metacognitive measures . Metacognition has been shown to predict learning performance . Learners who are metacognitively aware know what to do when they don't know what to do; that is, they have strategies for finding out or figuring out what they need to do. More importantly, research has demonstrated the value of metacognition in predicting academic achievement. For example, greater metacognitive ability has been linked to grade point average , math achievement , and reading skill . In addition to this, studies explicitly show that metacognitive skills play an important role in effective learning that leads to academic success, and that academically achieving students are better on metacognitive measures [11, 25, 28]
When evaluating or assessing their actions, both subjects consistently assess or evaluate the final results of their work correctly. The second is aware of the thought process that is done well when evaluating the results of his written work. Both subjects can evaluate the results of their work well because they are able to assess whether the final results obtained are correct and appropriate or not. When both subjects make mistakes, immediately realize the error after being given a question of metacognition when explaining the problem-solving procedure so that both subjects are able to answer all problems with the correct end result. Both subjects can also evaluate the results of their work because they are able to assess whether the final results obtained are correct and appropriate or not. Both subjects understand when the final result of their work is correct, both subjects will believe that it is indeed true and when the final result obtained is not correct, the subject feels hesitant and unsure of the answer even though the subject has tried to clarify the results of his written work several times make improvements to the answers. Furthermore, when evaluating their actions, subjects with low SLR also find it difficult to identify the strategies used. All subjects with low SLR were unable to mention alternatives to solving problems more effectively and assumed that the steps taken were the most effective steps. This is by the opinion of Kartika, Riyadi, and Sujadi (2015) that students are not aware of the thinking process that is done well when evaluating the results of work in writing because the subject always states that the results of the completion are correct even though there are still errors.
statistical reasoning, besides that misconceptions that cause errors or difficulties can trigger metacognition activities in monitoring and managing the problem-solving process . The results of this study indicate that metacognitive activities involved in the problem-solving process, but not all metacognitive activities are successful. Two forms of metacognitive failure were found, namely metacognitive blindness and metacognitive mirage. Metacognitive blindness occurs because metacognitive activities undertaken cannot detect errors. Errors that often occur are calculation errors, errors in applying procedures, and symbol errors . Calculation procedural errors relating to an individual's procedural knowledge about how to do something. At the transitional level, calculation procedural errors occur when determining the average delay, and at the quantitative level, procedural errors occur in combining two statistical measures. Metacognitive blindness occurs in calculation procedure, even though student checked the procedure, but failed to recognize the error , or occur when a person does not admit their error calculation process is done .
Only mathematics teacher not technological tools are the key change agents to bringing about reform in mathematics teaching with technology. Although preparing mathematics teachers to use technology appropriately is a complex task for teacher educators. The use of technology cannot replace conceptual understanding, computational fluency or problemsolving skills. In a balance mathematical progress the strategic use of technology enhance mathematics teaching and learning. Therefore, by taking technical support, we the teachers should try to meet the 21 st century trends and enable the students get ready for the better technical world ahead.
This article has contributed to the theoretical foundations of multi-agent systems by pre- senting a formal model of the cooperative problemsolving process. This four-stage model predicts and describes the circumstances under which agents will recognize the potential for cooperation, and how they will behave when this situation arises, from attempting to build a team, negotiating a collective plan, and acting as a team. We noted that this model is both abstract and idealized: there are cases that it does not consider, and no doubt some assump- tions have been made that are either too strong or too weak. Nevertheless, we are aware of no other attempt to formalize the cooperative problemsolving process in this way.
The Eulogio “Amang” Rodriguez Institute of Science and Technology, College of Arts and Science uses online collaborative problems solving using SNA as means to enhance the vital thinking skills in an on-line collaborative environment. The Teacher use online vital thinking case scenarios. The problem-solving skills questions will be posted and moderated by the teacher through online, and students are encouraged to engage in collaborative, and teamwork discussions regarding the problem-solving skills. The idea is to stimulate the student through the given case scenario and students discuss an idea, opinion and reasoning. The interactions were mostly instructor-centric, student- student interactions were scarce, most posts were repetitions of answers, and there were few discussions or reflections among participants. The researcher was used the study design followed an experimental, observational repeated measurement design approach. The course was monitored using real-time social network analysis; the data were collected by the end of the class and subsequently analyzed. An intervention plan was then formulated based on the analysis of the data or numerical effort point and modal graph.
and the item complexity can be placed on a single scale. So the position of an item in the proficiency scale can be located. This scale gives guidance in transforming students’ score into proficiencies interpretation for the skills they acquired. It means this scale enable us to provide students’ ability in terms of item difficulty level. It also provides us qualitative interpretation of students’ proficiencies. This proved that present problemsolving test can be used for numeric and qualitative descriptions of students’ proficiencies. These two descriptions can be used for different assessment purposes. These proficiencies descriptions usually depend on number of test items and their spread on the proficiency scale. In order to measure the consistency of the test it is required that the parallel test should be administered to calculate the reliability of the test. This was not the scope of the present study so the reliability dimensions of the test cannot be mathematically validated. As far as the validity is concerned external validity issues are still there in the test. In order to resolve these issues different samples of the students should be used to ensure the external validity but time constraint did not allow doing this. Different observations were taken for the validity of the test results to control internal validity issues. Relationship between item and its specific proficiency was first established by item writers and then it is empirically validated through data. Item fit statistics were used to check the validation of the extent to which this test measured the latent rate. One more step in this direction was the comparison of pilot and final test data. Same results were obtained for fit statistics.
The move away from Analytical ProblemSolving (see previous section) was motivated by the desire to adequately represent the complexity of our modern world and by the opportunity to simulate this complexity offered by computer-based assessment. In fact, computer-based assessment is able to go substantially beyond the pen-and-paper assess- ments that were employed in PISA 2003. More specifically, one of the sources of complexity is the increase in dynamic and interactive situations in our daily environments (Autor et al.; Funke, 2001; Greiff, 2012). Not only do software interfaces and their rapid change make continuous learning necessary, but also the way that specialized hardware confronts us with complex interactions: Mobile phones, ticket machines, electronic room access, copiers, and even washing machines now require sequences of interactions to set up these devices and to make them run. The common denominator of these examples is that a problem solver needs to actively interact with any kind of technical or nontechnical system, thereby generating the new information that is necessary to proceed successfully toward building a problem representation and carrying out a goal-directed solution process. However, the targeted type of dynamic situation is by no means limited to technical devices and can be extended even to social situations (cf. Collaborative ProblemSolving in the next section).
This is the classical problem, where we know the observer’s location and the direction toward the satellite (given by two spherical angles Q and F—astronomers call this the satel- lite’s position) at several (at least three) distinct times. There are many techniques of various degrees of sophistication for computing the orbital elements based on such data; here we can demonstrate only the simplest, brute-force approach.
Some linguists may feel that some positions, some syntactic relations, are natural, and that they tacitly interpret some elements in those positions. From the casual observation that, in some languages, some semantic relations between items are expressed by having these items occupy certain positions in a sentence, it is an easy step to assume that the mapping of semantic representations onto morphosyntax should be universally positional, i.e. that this is the ‘conceptually natural’ syntactic relation. But this idea faces the problem of accounting for all the cases in which languages use other means than linear juxtaposition to express relations, such as intonation, case marking, the use of loci in sign languages, etc. These conflicting facts force the adoption of a more complex model of grammar. For instance, case-marked elements typically have a relatively free ordering. This forces the adoption of costly constructs, such as assuming that Case-marked elements have a scrambling feature that induces pied-piping even after Case assignment, with the pied-piped element ‘attracted’ by a higher probe (Chomsky 2000). So Case-marking languages mysteriously happen to have extra mechan- isms that conspire to give the impression of a freer order. 20 The positional view
This problem is relatively simple, compared to other information which may be useful to a good player. (The problem is somewhat harder, even if three or more diamonds are needed in one hand, or if a particular card, for example a Queen, is needed with the four or five diamonds.) But we can find good approximations to the probabilities in this kind of problem, using a computer.