SINTAKS MODEL PEMBELAJARAN MATEMATIKA COLLABORATIVE PROBLEM SOLVING PADA MATERI SISTEM PERSAMAAN LINEAR TIGA VARIABEL (SPLTV)

Collaborative problem solving mathematics learning model aim for add new variation releated mathematics learning model which has been done by teachers. This learning model is new learning model which comes from a combination collaborative learning model and problem solving learning method. Based on the collaborative learning model supported by cognitive theory, social constructivism theory, and motivation theory. While the problem solving learning method supported by theory that refers to Polya steps. In addition to theories that support this learning model, a learning model includes approach, strategy, method, technique, and learning tactics. Collaborative problem solving learning model includes the following: (1) student centered approach; (2) discovery learning strategy and deductive learning strategy; (3) problem solving learning method; (4) study group technique; and (5) task LKPD tactics. After the collaborative problem solving learning model is formed, then the next step is making learning model syntax. Syntax of collaborative problem solving learning model is: (1) engagement; (2) exploration; (3) transformation; (4) solution; (5) presentation; and (6) reflection

How to stay perfect: the role of memory and behavioural traits in an experienced problem and a similar problem

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.When animals encounter a task they have solved previously, or the same problem appears in a different apparatus, how does memory, alongside behavioural traits such as persistence, selectivity and flexibility, enhance problem-solving efficiency? We examined this question by first presenting grey squirrels with a puzzle 22 months after their last experience of it (the recall task). Squirrels were then given the same problem presented in a physically different apparatus (the generalisation task) to test whether they would apply the previously learnt tactics to solve the same problem but in a different apparatus. The mean latency to success in the first trial of the recall task was significantly different from the first exposure but not different from the last exposure of the original task, showing retention of the task. A neophobia test in the generalisation task suggested squirrels perceived the different apparatus as a different problem, but they quickly came to apply the same effective tactics as before to solve the task. Greater selectivity (the proportion of effective behaviours) and flexibility (the rate of switching between tactics) both enhanced efficiency in the recall task, but only selectivity enhanced efficiency in the generalisation task. These results support the interaction between memory and behavioural traits in problem-solving, in particular memory of task-specific tactics that could enhance efficiency. Squirrels remembered and emitted task-effective tactics more than ineffective tactics. As a result, they consistently changed from ineffective to effective behaviours after failed attempts at problem-solving

The Effectiveness of Using Response- based Strategy in Enhancing Students' Levels of Engagement, Motivation, Problem-solving Skills, and Critical Thinking

The study analyzed the efficacy of response-based teaching tactics in enhancing students' levels of engagement, motivation, problem-solving skills, and critical thinking by utilizing data and a mixed-methods research methodology. There was a total of 30 students in the sample, 15 of whom were assigned to each of the two groups (response-based techniques and conventional instruction). Researchers also looked at how factors like teacher background and technological sophistication influenced the success of these techniques in the classroom. The students participated in a pre-test to define a starting point and a post-test to evaluate growth throughout the study. The efficacy of the response-based tactics was determined by statistical analysis using t-tests, ANOVA, and Cohen's d. Teacher and student demographics, as well as information about classroom practises and the usage of technology, were gathered through an online survey. Student perceptions of their own problem-solving and critical-thinking skills, as well as their level of classroom participation and motivation, were also probed in the survey. Both quantitative and qualitative approaches were employed to gather and analyze the data for this study. In terms of improving students' interest, motivation, problem-solving skills, and critical thinking, the results demonstrated that response-based teaching tactics were superior to more conventional methods. However, it was discovered that teacher traits and technology use influenced the efficacy of the response-based tactics. The findings also suggested that response-based teaching tactics might be useful in online classrooms, with the caveat that the success of such strategies would depend on the specific nature of the strategies employed and the specific features of the technology tools used. Conclusions from the study indicate that using response-based teaching methodologies can improve students' interest, motivation, problem-solving skills, and critical-thinking capacities. This study's methodology and statistical methods can serve as a foundation for other studies in this field. Keywords: response-based teaching strategies, traditional teaching approaches, student engagement, student motivation, problem-solving skills, critical thinking abilities DOI: 10.7176/JEP/14-27-10 Publication date:September 30th 2023

Law Enforcement as a Form of Development: Community Policing in the United States

The evolution of crime throughout time has required a development of alternative policing methodologies in the United States. One result of this is community policing, where problem solving became foundational to solving crime, transitioning from reactionary to prevention-based policing tactics. However, serious issues with community policing persist. This paper considers the role of police officers as development actors, introducing a foundational philosophy of development and formulating new principles and suggestions for modern police tactics

Critical thinking, problem solving : a unified framework for teaching a process approach.

This dissertation presents a unified framework to teach critical thinking and problem solving in a sixth grade computer classroom. In the context of this framework, problem solving is viewed as a critical thinking skill that also incorporates application of other critical thinking skills. Through a review of literature of critical thinking, problem solving, writing, Logo, simulations, and other related areas of study, we derive instructional principles important to consider when formulating a pedagogy to teach critical thinking/problem solving in a 6th grade computer classroom. We then present a rationale for a unified framework to teach critical thinking/problem solving and describe the said framework, titled, TACTICS (Tools ((to)) Assimilate Critical Thinking in Classroom Subjects). A process approach is advocated that includes an emphasis on the development of metacognition and an inquisitive spirit, the application of a general problem solving approach, and the use of specific heuristics. Through conscious use of critical thinking skills when applying problem solving strategies, students can learn to strengthen critical thinking and problem solving skills and come to see how the same general skills are used in a variety of circumstances. Eight instructional principles are suggested to teach critical thinking skills and to promote their generalization to other subject areas. These are supported by four problem solving tools designed to aid students in connecting their problem solving experiences in one area to their work in other subject areas. These tools are: Polya Four-Step General Problem Solving Approach, The Heuristic Bank, Student and Teacher-Made Reference Manuals and Students\u27 Journals. A curriculum resource book is included that demonstrates how the use of the TACTICS model can build critical thinking and problem solving skills when studying Logo, when writing or when using a simulation. Included are examples of curriculum unit and instructions for teachers to design their own curriculum units in their subject areas

CABINS: Case-based interactive scheduler

In this paper we discuss the need for interactive factory schedule repair and improvement, and we identify case-based reasoning (CBR) as an appropriate methodology. Case-based reasoning is the problem solving paradigm that relies on a memory for past problem solving experiences (cases) to guide current problem solving. Cases similar to the current case are retrieved from the case memory, and similarities and differences of the current case to past cases are identified. Then a best case is selected, and its repair plan is adapted to fit the current problem description. If a repair solution fails, an explanation for the failure is stored along with the case in memory, so that the user can avoid repeating similar failures in the future. So far we have identified a number of repair strategies and tactics for factory scheduling and have implemented a part of our approach in a prototype system, called CABINS. As a future work, we are going to scale up CABINS to evaluate its usefulness in a real manufacturing environment

Analyzing helicopter evasive maneuver effectiveness against rocket-propelled grenades

It has long been acknowledged that military helicopters are vulnerable to ground-launched threats, in particular, the RPG-7 rocket-propelled grenade. Current helicopter threat mitigation strategies rely on a combination of operational tactics and selectively placed armor plating, which can help to mitigate but not entirely remove the threat. However, in recent years, a number of active protection systems designed to protect land-based vehicles from rocket and missile fire have been developed. These systems all use a sensor suite to detect, track, and predict the threat trajectory, which is then employed in the computation of an intercept trajectory for a defensive kill mechanism. Although a complete active protection system in its current form is unsuitable for helicopters, in this paper, it is assumed that the active protection system’s track and threat trajectory prediction subsystem could be used offline as a tool to develop tactics and techniques to counter the threat from rocket-propelled grenade attacks. It is further proposed that such a maneuver can be found by solving a pursuit–evasion differential game. Because the first stage in solving this problem is developing the capability to evaluate the game, nonlinear dynamic and spatial models for a helicopter, RPG-7 round, and gunner, and evasion strategies were developed and integrated into a new simulation engine. Analysis of the results from representative vignettes demonstrates that the simulation yields the value of the engagement pursuit–evasion game. It is also shown that, in the majority of cases, survivability can be significantly improved by performing an appropriate evasive maneuver. Consequently, this simulation may be used as an important tool for both designing and evaluating evasive tactics and is the first step in designing a maneuver-based active protection system, leading to improved rotorcraft survivability

Making mathematics on paper : constructing representations of stories about related linear functions

This dissertation takes up the problem of applied quantitative inference as a central question for cognitive science, asking what must happen during problem solving for people to obtain a meaningful and effective representation of the problem. The core of the dissertation reports exploratory empirical studies that seek to answer the descriptive question of how quantitative inferences are generated, pursued, and evaluated by problem solvers with different mathematical backgrounds. These are framed against a controversy, described in Chapter 2, over the theoretical and empirical validity of current cognitive science accounts of problems, solutions, knowledge, and competent human activity outside of laboratory or school settings.Chapter 3 describes a written protocol study of algebra story problem solving among advanced undergraduates in computer science. A relatively open-ended interpretive framework for "problem-solving episodes" is developed and applied to their written solution attempts. The resulting description of problem-solving activities gives a surprising image of competence among an important occupational target for standard mathematics instruction.Chapter 4 follows these results into detailed verbal problem-solving interviews with algebra students and teachers. These provide a comparison across settings and levels of competence for the same set of problems. The results corroborate similar generative activities outside the standard formalism of algebra across levels of competence. Notable among these nonalgebraic problem-solving activities are "model-based reasoning tactics," in which people reason about quantitative relations in terms of the dimensional structure of functional relations described in the problem. These tactics support different activities within surrounding solution attempts and usually describe "states" in the problem's situational structure.Chapter 5 holds these activities accountable to local combinations of notation and quantity, reinterpreting results for model-based reasoning in an ecological analysis of material designs for constructing and evaluating quantitative inferences. This analysis brings forward important relations between what material designs afford problem solvers and the complexity of episodic structure observed in their solution attempts. The dissertation closes with a reappraisal of the relationship between knowledge, person, and setting and, I will argue, puts us on a more promising track for a descriptively adequate theoretical account of constructing mathematical representations that support applied quantitative inference

How practice makes perfect: the role of persistence, flexibility and learning in problem solving efficiency

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.To fully understand how problem solving ability provides adaptive advantages for animals, we should understand the mechanisms that support this ability. Recent studies have highlighted several behavioural traits including persistence, behavioural variety and behavioural/cognitive flexibility that contribute to problem solving success. However, any increment in these traits will increase time and energy costs in natural conditions, so they are not necessarily advantageous. To examine how behavioural traits vary during learning to solve a problem efficiently, we gave grey squirrels (Sciurus carolinensis) a problem solving task that required squirrels to obtain out-of-reach but visible hazelnuts by making a lever drop in the laboratory. We recorded persistence, measured as attempt rate, flexibility, measured as the rate of switching between tactics, and behavioural selectivity, measured as the proportion of effective behaviours, in relation to problem solving efficiency on a trial-by-trial basis. Persistence and behavioural selectivity were found to be directly associated with problem solving efficiency. These two factors also mediated the effects of flexibility and increased experience. We also found two routes that led to more efficient problem solving across learning trials: increasing persistence or increasing behavioural selectivity. Flexibility was independent from learning. Flexibility could increase problem solving efficiency, but it also has a time cost; furthermore it seemed to involve a trade-off with behavioural selectivity, with high flexibility being associated with a higher frequency of some disadvantageous ineffective behaviours. These results suggest that flexibility is an independent cognitive process or behavioural trait that may not always bring advantages to animals

A Man-Machine Competitive Game: A Naval Duel

The research reported here is the development of a man-machine game in which the competitors are the captain of a submarine and the commander of an opposing task force. This naval game has been implemented and tested in the Problem Solving Facility of the University of Pennsylvania under Contract NOnr 551(48) sponsored by the Methodology Division, Office of Naval Research. The broad objective of this research has been to experiment with and develop a man-machine framework in which an executive, scientist or engineer may employ strategies and tactics in an operational environment. A complete functional description of the game will be given in this report. This chapter provides an overview of the game and cites its salient characteristics. Chapter 2 presents the game through a play-by-play record of one competitor in an actual duel. Chapter 3 presents the various aspects of the Problem Solving methodology and developed tactics by means of three annotated duels. This also illustrates the versatility of the game and demonstrates the competitors\u27 capability to interact with the computer. Chapter 4 summarizes our research to date and lists planned refinements to the game. Additional documentation of the game structure is provided in the appendices