Use of worked‐example videos to support problem‐solving: An analysis of student behavior

2021 ◽  
Author(s):  
Chuhao Wu ◽  
Jennifer DeBoer ◽  
Jeffrey F. Rhoads ◽  
Edward Berger
Keyword(s):  
Problem Solving ◽  
Student Behavior ◽  
Worked Example

Complex Problem Solving and Worked Examples

2009 ◽  
Vol 23 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Florian Schmidt-Weigand ◽  
Martin Hänze ◽  
Rita Wodzinski
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Experience ◽  
Worked Examples ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Strategic Learning ◽  
Learning Behavior ◽  
Worked Example ◽  
8Th Grade

How can worked examples be enhanced to promote complex problem solving? N = 92 students of the 8th grade attended in pairs to a physics problem. Problem solving was supported by (a) a worked example given as a whole, (b) a worked example presented incrementally (i.e. only one solution step at a time), or (c) a worked example presented incrementally and accompanied by strategic prompts. In groups (b) and (c) students self-regulated when to attend to the next solution step. In group (c) each solution step was preceded by a prompt that suggested strategic learning behavior (e.g. note taking, sketching, communicating with the learning partner, etc.). Prompts and solution steps were given on separate sheets. The study revealed that incremental presentation lead to a better learning experience (higher feeling of competence, lower cognitive load) compared to a conventional presentation of the worked example. However, only if additional strategic learning behavior was prompted, students remembered the solution more correctly and reproduced more solution steps.

scholarly journals Effects of Problem Solving after Worked Example Study on Primary School Children's Monitoring Accuracy

2014 ◽  
Vol 28 (3) ◽  
pp. 382-391 ◽  
Author(s):  
Martine Baars ◽  
Tamara van Gog ◽  
Anique de Bruin ◽  
Fred Paas
Keyword(s):  
Problem Solving ◽  
Primary School ◽  
Worked Example ◽  
Monitoring Accuracy

scholarly journals EXPERIENCE STUDENT BACKGROUND AND THEIR BEHAVIOR IN PROBLEM SOLVING

2020 ◽  
Vol 9 (1) ◽  
pp. 59
Author(s):  
Yulyanti Harisman ◽  
Muchamad Subali Noto ◽  
Wahyu Hidayat
Keyword(s):  
High Schools ◽  
Problem Solving ◽  
Student Behavior ◽  
Junior High ◽  
Mathematical Problem ◽  
Junior High Schools ◽  
Teacher Professionalism ◽  
Survey Method ◽  
Mathematical Problem Solving ◽  
Problem Solving Behavior

These Students' mathematical problem solving behavior had been presented in the previous paper. Four categories of students' mathematical problem solving behavior in junior high schools in Indonesia had been obtained. These categories were: naive, routine, semi-sophisticated, and sophisticated. This paper was a continuation of that research. In this session would discuss about external aspects affect student behavior in problem solving. This research used survey method. Eighteen students from three junior high schools in Indonesia had been interviewed about it. These three aspects were: distance of home from school, family background, Contests-contests like math Olympiads that had been followed. The interview results were coded to get conclusions. Research findings were that the external aspects of students did not influence students in behaving to solve problems in mathematics. the implication of this finding is that the main factor influencing student behavior in problem solving is teacher professionalism in learning not from the students themselves, so the teacher must be really prepared in designing all components of learning well.

scholarly journals An Analysis of the Statistics Content in Nepali School Textbooks

2019 ◽  
Vol 4 (4) ◽  
pp. 21-34
Author(s):  
Dustin L. Jones ◽  
Deepak Basyal
Keyword(s):  
Problem Solving ◽  
Statistical Problem ◽  
The Other ◽  
Mathematics Textbooks ◽  
Text Book ◽  
Worked Example ◽  
Analysis Phase ◽  
Mathematical Calculation ◽  
Analyse Data ◽  
Problem Solving Process

To determine the nature and extent of the statistics content that Nepali students may learn in school, we examined mathematics textbooks for grades 4-10 from five different publishers. All of the tasks in each statistics chapter were examined, for a total of 1755 tasks across 35 textbooks. Each task was classified according to the phases of the statistical problem-solving process (formulate questions, collect data, analyse data, interpret results) that were addressed. Nearly every task required students to analyse data; the other phases were rarely addressed. Additionally, tasks addressing the analysis phase were coded according to analysis activities; the majority of these tasks required students to read a display and perform a mathematical calculation. For each series, at least two-thirds of the statistics tasks followed a similar worked example. Based on these findings, we offer recommendations for teachers, text book writers, and the Curriculum Development Centre.

scholarly journals Efektivitas worked example dengan strategi pengelompokan siswa ditinjau dari kemampuan pemecahan masalah dan cognitive load

2019 ◽  
Vol 6 (1) ◽  
pp. 62-74
Author(s):  
Muhammad Ferry Irwansyah ◽  
Endah Retnowati
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Strategies ◽  
Worked Example ◽  
Grouping Strategy ◽  
Significant Difference ◽  
Grouping Students ◽  
8Th Grade ◽  
Problem Solving Strategies ◽  
Study Participants

Pada penelitian ini bertujuan untuk mendeskripsikan dan membandingkan efektivitas strategi pembelajaran worked example dan problem solving dengan strategi pengelompokan siswa (kolaboratif dan individual) ditinjau dari kemampuan pemecahan masalah dan cognitive load. Penelitian ini melibatkan 64 siswa kelas 8 sebagai partisipan penelitian yang dibagi menjadi empat kelompok secara acak dengan menggunakan desain eksperimen 2 × 2 (worked example vs. problem solving) × kolaboratif vs. individual). Hasil penelitian ini mengindikasikan bahwa tidak terdapat perbedaan signifikan penerapan strategi worked example dengan pengelompokan kolaboratif dan individual ditinjau dari kemampuan pemecahan masalah. Ditinjau dari cognitive load, strategi worked example efektif ketika siswa belajar individual, namun tidak efektif ketika siswa belajar secara kolaboratif. Ketika siswa belajar secara individual, strategi worked example dapat mengaktifkan cognitive load lebih rendah daripada strategi problem solving, sedangkan ketika siswa belajar secara kolaboratif, strategi worked example dan problem solving tidak berbeda dalam mereduksi cognitive load. The effectiveness of worked example with students’ grouping strategy viewed from problem-solving abilities and cognitive load AbstractThe study aimed to describe and compare the effectiveness of learning strategies (worked example and problem-solving) with the strategy of grouping students (collaborative and individual) viewed from problem-solving abilities and cognitive load. There were 64 of 8th-grade students as study participants divided into four groups randomly using experimental design 2 × 2 (worked example vs. problem-solving × collaborative vs. individual). The results of the study indicate that there is no significant difference implementation of worked example strategy between the collaborative strategies and individuals viewed from problem-solving abilities. Viewed from the cognitive load, the worked example strategy was effective when students learn individually, but it was not effective when students learn collaboratively. When students learn individually, worked example strategies could activate cognitive load lower than problem-solving strategies, whereas when students learn collaboratively, worked example strategies and problem-solving were no different in reducing cognitive load.

scholarly journals SISTEM PAKAR UNTUK MENGIDENTIFIKASI PERILAKU DAN KEPRIBADIAN SISWA PADA SMK NEGERI 2 TANGERANG

2019 ◽  
Vol 16 (2) ◽  
pp. 85-92
Author(s):  
Fifit Alfiah ◽  
Muhammad Arba Adnandi ◽  
Allyufi Fazril Rasyidin
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Problem Solving ◽  
Human Resources ◽  
Student Teachers ◽  
Student Behavior ◽  
Learning Activities ◽  
Improve Quality ◽  
And Behavior ◽  
Guidance Teachers

The number of problems in learning activities such as the personality and behavior of students who are less good towards the teacher and there is still many students who are confused in their personality in deciding to continue their studies with a department in accordance with their education or personality. So the author aims to create one expert system that can help counseling and student teachers in determining personality and good behavior for students who are not good or to determine in continuing college studies with one of the knowledge that can help humans in making decisions, namely expert systems. Expert system (Expert System) is a part of artificial intelligence that contains knowledge and experience that is input into the knowledge base. In designing this expert system, the author uses a forward chaining technique because problem-solving is done by collecting data and then drawing a conclusion. The results of this expert system are able to help psychology or Counseling Guidance teachers in analyzing student behavior to improve quality human resources by testing the diagnosis of student behavior and personality with expert system applications to produce solutions that meet their needs with a percentage above 90%.

scholarly journals Data-Driven Intelligent Tutoring Systems for STEM+C Learning and Teaching

2019 ◽  
Vol 4 (9) ◽  
pp. 202-206
Author(s):  
Hieu Trong Bui
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Student Behavior ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Data Driven ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
Past Data ◽  
Problem Solving Behavior

It is wide known that one of the most effective ways to learn is through problem solving. In recent years, it is widely known that problem solving is a central subject and fundamental ability in the teaching and learning. Besides, problem solving is integrated in the STEM+C (Science, Technology, Engineering, and Math plus Computing, Coding or Computer Science) fields. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. Intelligent tutoring systems provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. In this work, the main goal of this paper is to: 1) present ITSs used in the STEM+C education; and 2) introduce data-driven ITSs for STEM+C education.

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