Solve Problems In Practice Research Articles

After initial acquisition, problem-solving leads to better long-term performance than example study, even for complex tasks

BackgroundSuccessful implementation of effective acquisition strategies (e.g., example study, problem-solving) could help improve mathematics performance. However, it is not yet fully understood when each acquisition strategy should be used, despite the practical value of this knowledge for mathematics textbook authors, teachers, and students. AimsBuilding upon two recent perspectives on when example study and problem-solving are beneficial, we proposed that the optimal acquisition strategy could depend on both task complexity and retention interval (i.e., time between the final practice opportunity and the test). We conducted a multi-classroom experiment to test this proposition. Sample166 typically-developing Dutch fifth-grade students participated (Mage = 11.14 years; 42.2% boys). MethodsWe used a 2 (Task Complexity: simple vs. complex) x 2 (Acquisition Strategy: example study vs. problem-solving) x 2 (Retention Interval: 5 min vs. 1 week) between-subjects design with problem-solving performance as dependent variable. ResultsThere was no evidence for the hypothesised three-way interaction effect of task complexity, acquisition strategy, and retention interval. However, there was evidence for the hypothesised two-way interaction effect of acquisition strategy and retention interval, independent of task complexity. More specifically, after 5 min, there was no statistically significant performance difference between students studying worked examples and those solving practice problems, but after 1 week, students solving practice problems outperformed those studying worked examples. ConclusionsOur findings imply that, after initial acquisition, problem-solving leads to better long-term problem-solving performance than example study. This holds true even for a relatively complex task and with limited instruction.

APPROACH TO ENSURING COST-EFFECTIVENESS OF DESIGN WORKS FOR THE ENTIRE CYCLE OF TECHNICAL PREPARATION OF PRODUCTION BY CUTTING

It is fundamentally impossible to transform the knowledge of technical: design checkpoint, technological CCI (OP) of production by cutting by the functions of control elements: systematization, formalization, unification, grouping for the transition to an information deterministic technology for solving practice problems throughout the cycle of a high level of automation with only elements (means) of integrating the design element base. You need your complete composition of the main elements of the integration of the technological element base of organic in form, content and sequence of knowledge transformation. By means of the technological element base, the hierarchical structure of the knowledge base (CCI (OP), KPP) was determined in seven levels of the structure of its basic objects of functionally different types. All basic knowledge objects are defined by the functions of classical knowledge of the organization of complex systems: quantity, quality, informational similarity, dimension. This knowledge in the in-formation technology of solving technical and design problems is distributed in two directories: organization systems – in the main operations of the technological direction; control systems are – in two aux-iliary design direction operations. It is fundamentally important for each area to determine its composition of updated methods, methods, tools, algorithms, operations for solving the necessary problems and provide them with the left-hand properties of high economic efficiency according to the functions of achieving knowledge and flexibility.

An improvement of newton – krylov method for solution of nonlinear equations

Solving problems in practice often results in a system of nonlinear equations with a large number of equations and unknowns. Finding the exact solution to this class of equations is very difficult and almost impossible. Recently, with the development of technology, many methods and algorithms have been proposed to approximate the class of these systems of equations. Especially the third-order Newton–Krylov method has solved quite well this class of systems of equations with the third degree of convergence. In this paper, we present a new improvement of the third-order Newton-Krylov method with a quaternary convergence rate and prove the convergence of the iterative formula. In addition, the paper also presents an experimental result to demonstrate the convergence speed of the method

INCREASED LOAD OF RECONFIGURED MACHINING MACHINES BASED ON PROGRESSIVE FORMS OF SOCIAL DIVISION OF LABOR

In the context of the rapid informatization of all spheres of human life in the subject area, it is fundamentally important to move on to an information deterministic high level of automation of technology for solving practice problems throughout its cycle. For this, the base of the normative reference information and the fundamentally new knowledge base of the CCI (OP) on the seven levels of the structure of its basic objects of the necessary types are proposed in a new reading. All basic knowledge objects are defined the functions of solving their problems in co-corresponding information models. The organization of information and logical connections between information models is based on the identifiers of their connection. The approach to determining all basic objects of knowledge is based on the functions of classical knowledge of the organization of complex systems in their full composition: (quantity, quality, informational similarity, dimension). As a result, a technological information base has been prepared for the transition to the most progressive economically competitive form of organizing CCI (OP) and production. In the conditions of market relations in the economy of functionally different cutting processes, with an increase in multinomenclature in the structure of each of the industries and the composition of the functions of the working machines used with program control, this is fundamentally important. Solving the set tasks aktually, almost significantly, has scientific novelty.

Differential instructional effectiveness: overcoming the challenge of learning to solve trigonometry problems that involved algebraic transformation skills

The design principles of cognitive load theory and learning by analogy has independently contributed to our understanding why an instruction will or will not work. In an experimental study involving 97 Year 9 Australian students conducted in regular classrooms, we evaluated the effect of the unguided problem-solving approach, worked examples approach and analogy approach on learning to solve two types of trigonometry problem. These trigonometry problems (sin40° = frac{x}{6} vs. cos50° = frac{14}{x}) exhibited two levels of complexity owing to the location of the pronumeral (numerator vs. denominator). The solution procedure of worked examples provided guidance, whereas the unguided problem-solving was without any guidance. Analogical learning placed emphasis on comparing a pair of isomorphic examples to facilitate transfer. Across the three approaches, solving practice problems contributed towards performance on the post-test. However, the worked examples approach and analogy approach were more efficient than the unguided problem-solving approach for acquiring skills to solve practice problems regardless of their complexity. Therefore, the worked examples approach and analogy approach that emphasizes algebraic transformation skills have the potential to reform instructional efficiency for learning to solve trigonometry problems.

Studying while anxious: mathematics anxiety and the avoidance of solving practice problems during exam preparation in college calculus

Studying while anxious: mathematics anxiety and the avoidance of solving practice problems during exam preparation in college calculus

PERAN MODEL PEMBELAJARAN TUTOR SEBAYA TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS 5 SDN 2 TAHUNAN

This study aims to determine the effect of the Tutor's Learning Method Peer to the mathematics learning outcomes of fifth graders of bar chart material in grade 5 SD N 2 Tahunan. In this study, the type of research used was a descriptive qualitative. Qualitative data analysis techniques were chosen in this study because the data analysis process did not involve or take the form of numbers. The results of the research from observations when it was carried out at SD N 2 Tahunan, several problems were found including, low student learning outcomes, lack of knowledge about the material being taught and weak memorization of multiplication formulas as well as errors in working on questions that occurreddifficulty in applying the formula to solving practice problems

Instructing students on effective sequences of examples and problems: Does self‐regulated learning improve from knowing what works and why?

AbstractNowadays, students often practice problem‐solving skills in online learning environments with the help of examples and problems. This requires them to self‐regulate their learning. It is questionable how novices self‐regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem‐solving procedure by fixed sequences of examples and problems (FS‐condition), or by self‐regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL‐condition) or without (SRL‐condition) watching a video detailing the instructional design principles. Students' task‐selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL‐condition than in the SRL‐condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems‐solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow‐up research should investigate how we can further improve self‐regulated learning from examples and practice problems.

Reading Versus Doing: Methods of Teaching Problem-Solving in Introductory Statistics

Practice problems and worked examples are both well-established teaching techniques. Research in math and physics suggests that having students study worked examples during their first contact with new material, instead of solving practice problems, can be beneficial to their subsequent performance, possibly due to the reduced cognitive load required to study examples compared to generating solutions. However, there is minimal research directly comparing these teaching methods in introductory statistics. In this study, we chose six pairs of introductory statistics topics of approximately equal difficulty from throughout the semester. After an initial mini-lecture, one topic from each pair was taught using practice problems; the other was taught by having students read worked examples. Using Bayesian and frequentist analyses, we find that student performance is better after reading worked examples. This may be due to worked examples slowing the process of forgetting. Surprisingly, there is also strong evidence from in-class surveys that students experience greater frustration when reading worked examples. This could indicate that frustration is not an effective proxy for cognitive load. Alternatively, it could indicate that classroom supports during in-class problem-solving were effective in reducing the cognitive load of practice problems below that of interpreting written statistical explanations.

Differential Effects of Incentivized Homework on Student Achievement in Undergraduate Mathematics

We present an analysis on the differential effects of incentivizing homework in an introductory mathematics course at the United States Military Academy. We found that including homework as part of a student’s overall course average (incentive) led to a significantly higher performance (achievement) on homework assignments. However, doing homework only led to a significantly higher exam performance for medium-ability students. One possible explanation for our results is high-ability students may be able to grasp material with minimal out-of-class practice; medium-ability students benefit significantly from solving practice problems; and low-ability students may have difficulty in understanding even if they are actively practicing problems.

Effects of problem\u2013example and example\u2013problem pairs on gifted and nongifted primary school students\u2019 learning

Example-based learning (i.e., studying examples to learn a problem–solution procedure, often alternated with solving practice problems) leads to better learning outcomes than solving practice problems only, and video examples are increasingly being used in online and blended learning environments. Recent findings show that the presentation order of examples and problems affects learning: Example–problem pairs have been found to be more effective than problem–example pairs. We investigated a motivational explanation for this difference, which states that starting with a practice problem might be too difficult, causing learners to lose confidence and motivation to study. We investigated this by presenting gifted (n = 61) and nongifted primary school students (n = 65) with two problem–example or example–problem pairs. We hypothesized that gifted students, who generally report higher perceived competence and autonomy and higher need for cognition, would be less affected by the difficulty of starting with a problem. As expected, gifted students indeed reported higher motivation and confidence than nongifted students, and gifted students were more efficient learners. In contrast to our expectations, however, there was no difference between gifted and nongifted students in the effect of the different task sequences on test performance. Studying example–problem pairs was more efficient than studying problem–example pairs, both for gifted and nongifted students.

Benefits of adding anxiety-reducing features to a computer-based multimedia lesson on statistics

The present study examined the effectiveness of techniques intended to reduce anxiety as students learn mathematical content from a computer-based lesson. In a between-subjects experiment, students learned statistical rules through worked examples in a computer-based learning environment that either did (treatment group) or did not (control group) include anxiety reducing features—a coping message delivered through the lesson by an online pedagogical agent concerning how to manage feelings of anxiety, and prompts for expressive writing, in which students summarize their thoughts and feelings. An independent samples t-test showed that the treatment group, which received added anxiety-reducing features, showed higher accuracy than the control group on solving practice problems (d = 0.71) and retention problems (d = 0.63) and reported higher perceived effort on learning the multimedia lesson (d = 0.66). In addition, a standard multiple linear regression found that anxiety, self-efficacy, and cognitive load as a set predicted performance (R2 = 0.56), with self-efficacy as the strongest predictor (β = 0.63). Adding anxiety-reducing features to an online lesson may encourage greater effort, which leads to better learning outcomes.

Professional doctorate and professional nursing practice

The professional doctorate degree in nursing is a novel approach to gaining a doctoral qualification other than the traditional Doctor of Philosophy (PhD) degree. This taught programme aims at linking theory and practice innovations in addressing clinical nursing problems. This paper outlines the evolution of the professional doctorate in the USA, the UK and Australia and its differences from the traditional PhD. I argue that nurses enrolled in this course work programme will not only have the opportunity to pursue studies at the doctoral level, but it will also facilitate nurses to challenge and share their experiences in solving practice problems in a collegial manner. By understanding the broader perspective of an environment dominated by scarce resources and intricate socio-political relationship, nurses will be able to influence policy decisions and ultimately improve the standard of health care and enhance the professional status of nursing. It may even reduce the antagonism of the anti-intellectual environment of the workplace through practice-oriented research.

Research in school nursing practice: basic steps in nursing research.

Research is essential to every nurse's clinical practice. There is a reciprocity between nursing practice and research; they inform each other, producing insights about nursing interventions and the impact of nursing interventions on the lives of nursing clients. Nursing research has become a major force in changing nursing practice by solving practice problems and by increasing the body of nursing knowledge. Understanding the research process can assist school nurses to identify the knowledge embedded in the clinical work of other school nurses. This article is directed toward increasing that understanding through a discussion of the general steps of the quantitative research process.

Increasing Mathematics Confidence by Using Worked Examples

An important way to help our students learn and strengthen their knowledge of mathematics is by studying already-worked examples of problems. A considerable amount of evidence suggests that analyzing worked-out problems can be as conducive to learning as actually solving practice problems and that it may be more effective in helping students to recognize underlying similarities between problems (Sweller 1989; Zhu and Simon 1987; Sweller and Cooper 1985). Well-constructed worked examples do more than merely teach rote procedures; they illustrate mathematical principles and classes of problem situations