Examining the effects of students' self-efficacy and prior knowledge on learning and visual behavior in a physics game

Opportunity Recognition (OR) memiliki peranan penting dalam dunia wirausaha. Dalam penelitian terdahulu, OR sering disebutkan sebagai langkah kunci dalam proses kewirausahaan. Penelitian ini bertujuan untuk mengetahui seberapa signifikan pengaruh dan arah hubungan Self-Efficacy (SE), Prior Knowledge (PK), Social Network (SN) dan terhadap OR dengan mediasi Entrepreneurial Alertness (EA). Objek pada penelitian adalah UMKM bidang kuliner binaan Dinas KUKM Kota Bandung. Data dalam penelitian ini didapat melalui penyebaran kuesioner secara online kepada 339 sampel dari 2221 populasi UMKM bidang kuliner binaan Dinas KUKM Kota Bandung. Kuesioner terdiri atas beberapa pernyataan dengan pilihan jawaban berupa 5 skala likert. Dalam pengujian hipotesis menunjukkan bahwa semua faktor secara positif berhubungan dengan OR. Selain itu hasil menunjukkan bahwa SE tidak mempengaruhi OR secara signifikan, dan yang memiliki pengaruh paling signifikan adalah SN.

Download Full-text

Design Factors for Effective Science Simulations

Discoveries in Gaming and Computer-Mediated Simulations ◽

10.4018/978-1-60960-565-0.ch002 ◽

2011 ◽

pp. 16-35

Author(s):

Jan L. Plass ◽

Bruce D. Homer ◽

Catherine Milne ◽

Trace Jordan ◽

Slava Kalyuga ◽

...

Keyword(s):

Cognitive Load ◽

Prior Knowledge ◽

Self Efficacy ◽

Cognitive Learning ◽

Design Features ◽

Chemistry Students ◽

Novice Learners ◽

Effective Science ◽

Iconic Representations ◽

Design Factors

We argue that the effectiveness of simulations for science education depends on design features such as the type of representation chosen to depict key concepts. We hypothesize that the addition of iconic representations to simulations can help novice learners interpret the visual simulation interface and improve cognitive learning outcomes as well as learners’ self-efficacy. This hypothesis was tested in two experiments with high school chemistry students. The studies examined the effects of representation type (symbolic versus iconic), prior knowledge, and spatial ability on comprehension, knowledge transfer, and self-efficacy under low cognitive load (Study 1, N=80) and high cognitive load conditions (Study 2, N=91). Results supported our hypotheses that design features such as the addition of iconic representations can help scaffold students’ comprehension of science simulations, and that this effect was strongest for learners with low prior knowledge. Adding icons also improved learners’ general self-efficacy.

Download Full-text

Teaching Metacognition Experientially

Teaching of Psychology ◽

10.1177/0098628317727633 ◽

2017 ◽

Vol 44 (4) ◽

pp. 298-305 ◽

Cited By ~ 3

Author(s):

Aaron S. Richmond ◽

Anastasia M. Bacca ◽

Jared S. Becknell ◽

Ryan P. Coyle

Keyword(s):

Experiential Learning ◽

Prior Knowledge ◽

Direct Instruction ◽

Self Efficacy ◽

Psychology Students ◽

Introductory Psychology ◽

Process Information ◽

New Information ◽

Course Material ◽

Past Experiences

We investigated the effects of using experiential learning and direct instruction to teach metacognitive theory and to determine whether instructional type differentially affected higher vs. lower level learning. We randomly assigned 87 introductory psychology students to either experiential learning or direct instruction conditions. We pretested participant’s knowledge of metacognitive theory, and then participants received either experiential or direct instruction, after which they completed a posttest of knowledge of metacognitive theory. After covarying prior knowledge, data suggested that experiential learning may be more effective than direct instruction for teaching metacognitive theory, particularly for higher level recall and recognition assessments. Our results suggest that when taught using experiential learning, students may process information at a deeper level and recall more information because they may have related new information to their past experiences, engaged in the course material, and may have increased self-efficacy for the learned material.

Download Full-text

Using Digital Games in the Science Classroom

10.18122/td.1806.boisestate ◽

2021 ◽

Author(s):

Tamara L. Anderson

Keyword(s):

High School ◽

Prior Knowledge ◽

Self Efficacy ◽

Science Classroom ◽

Digital Games ◽

School Science ◽

High School Science ◽

Teachers And Students ◽

Science Classes ◽

Scaffolding Learning

High school science classes can be difficult for students to be successful in because of the content-specific vocabulary and the expectation of prior knowledge in the subject area that teachers have of their students. The use of digital games in the classroom can provide teachers with the tools to help students scaffold their learning and better grasp the vocabulary necessary to be successful in science class. The purpose of this mixed methods study was to focus teachers’ and students’ perceptions of digital games in the high school science classroom on vocabulary development, scaffolding learning by activating prior knowledge, and self-efficacy. Findings suggest that teachers and students believed that using digital games positively impacted the development of vocabulary knowledge and helped scaffolding learning. Some students found that their levels of self-efficacy were positively impacted by using digital games in their science classes. Teachers can use these findings to make informed decisions about how to integrate digital games into their science curriculum.

Download Full-text

PENGARUH PENGETAHUAN AWAL AKUNTANSI DAN EFIKASI DIRI TERHADAP TINGKAT PEMAHAMAN AKUNTANSI MELALUI MINAT BELAJAR PADA MAHASISWA JURUSAN AKUNTANSI UNIVERSITAS PGRI ADI BUANA SURABAYA

JURNAL EKONOMI PENDIDIKAN DAN KEWIRAUSAHAAN ◽

10.26740/jepk.v3n2.p181-196 ◽

2017 ◽

Vol 3 (2) ◽

pp. 181

Author(s):

Mohammad Taufiq

Keyword(s):

Structural Equation Modeling ◽

Prior Knowledge ◽

Self Efficacy ◽

Structural Equation ◽

Equation Modeling ◽

Level Of Understanding ◽

Learning Interest

This study was conducted to analyze the effect of accounting prior knowledge, self-efficacy, and interest in the level of understanding of accounting majors accounting PGRI Adi Buana University of Surabaya. The approach used in this research is quantitative. Populations are the second semester students 2014/2015 Accounting Department of Economics, PGRI Adi Buana University with a sample of 156 students. Data were analyzed using Structural Equation Modeling (SEM). The results showed that there was a significant effect of prior knowledge of accounting and self efficacy on the interest in learning, and learning interest influencing the level of understanding

Download Full-text

Making Teaching Relevant: Enhancing Students’ Self-Efficacy Through Teachers’ Enthusiasm for More Active Classroom Engagement

International Journal of Contemporary Education ◽

10.11114/ijce.v3i2.4882 ◽

2020 ◽

Vol 3 (2) ◽

pp. 30

Author(s):

Hany Zaky

Keyword(s):

Prior Knowledge ◽

Self Efficacy ◽

Sense Of Self ◽

Mastery Learning ◽

Learning Processes ◽

Strong Sense ◽

Classroom Engagement ◽

Knowledge And Skills ◽

Learning And Teaching ◽

High Assurance

A strong sense of self-efficacy supports human accomplishment. Learners with high assurance in their capacities could approach difficult tasks as challenges to be handled rather than threats to be avoided. Such an outlook fosters those learners’ activities’ engrossment and their intrinsic interests of the learned contents. Therefore, learners set themselves goals and keep a strong commitment to these designated goals. To face failure, learners heighten and sustain their efforts by attributing this failure to insufficient effort and deficient knowledge and skills which ought to be acquired. To this end, those learners utilize their prior knowledge assurance to overcome the confronted threats.Concurrently, teachers ought to provide the relevant instruction that empowers their students. Consequently, learners could raise their expectations of the relevance of the perceived cognition and make sense of their world. Make learning relevance deemed a catalyst towards learners’ self- efficacy, motivation, and engagement with the learning processes. This article examines the irrevocable relationship between learners’ self- efficacy and their learning relevance towards mastery learning and active classroom engagement. The article raises some teaching challenges and suggests some research-based strategies to help teachers appreciate the broad panorama of learning and teaching in classrooms characterized by teaching self-efficacy and enthusiasm.

Download Full-text

Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

European Journal of Open Distance and E-Learning ◽

10.2478/eurodl-2014-0008 ◽

2014 ◽

Vol 17 (1) ◽

pp. 118-133 ◽

Cited By ~ 71

Author(s):

Erman Yukselturk ◽

Serhat Ozekes ◽

Yalın Kılıç Türel

Keyword(s):

Data Mining ◽

Online Learning ◽

Locus Of Control ◽

Online Education ◽

Prior Knowledge ◽

Self Efficacy ◽

Feature Selection Method ◽

Online Information ◽

Online Technologies ◽

Online Experience

Abstract This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.

Download Full-text