Learning performance and cognitive load in mobile learning: Impact of interaction complexity.
In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context‐aware environment. This paper presents a mobile application based on expert system (Plant‐E) for students to acquire knowledge about pla...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 6; pp. 917 - 928 |
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| Autores principales: | , , |
| Formato: | algorithm consumer/patient teaching materials questionnaire/scale research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132914556&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132914556 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2018 vid: 34 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 132914556 132914556 132914556 10.1111/jcal.12300 132914556 ppf: 917 ppct: 11 formats: tig: atl: Learning performance and cognitive load in mobile learning: Impact of interaction complexity. aug: au: Wang, Cixiao Fang, Ting Miao, Rong affil: Graduate School of Education, Peking University, Beijing China sug: subj: Mobile Applications Utilization Student Knowledge Decision Making Learning Learning Methods Student Performance Appraisal Cognition Academic Performance Pretest-Posttest Design Student Attitudes Memory User-Computer Interface Self-Efficacy China Child Adult Cognitive Therapy Quasi-Experimental Studies Questionnaires One-Way Analysis of Variance Descriptive Statistics T-Tests Scales Post Hoc Analysis Human Interaction (Research) Child: 6-12 years Adult: 19-44 years ab: In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context‐aware environment. This paper presents a mobile application based on expert system (Plant‐E) for students to acquire knowledge about plant classification by answering decision‐making questions. In order to study effectiveness of Plant‐E and cognitive load of students who experience different kinds of interaction in learning process, another mobile application (Plant‐G) only providing information pages of plants was developed. A quasi‐experiment was conducted with three classes of 137 seventh graders. The three classes, Class A with 46 students using Plant‐E in campus, Class B with 44 students using Plant‐G in campus, and Class C with 47 students using Plant‐G in a traditional classroom, constitute three groups with different interaction complexity. The research conducted pretest, posttest, and delayed test to evaluate learning performance of students in three classes and used a questionnaire to investigate their perceptions and attitudes towards proposed system. Results show that interaction complexity has an impact on students' learning performance and mental effort in mobile learning; the higher the interaction complexity is, the higher mental effort and the better learning performance in mobile learning will be. Lay Description: What is already known about this topic: Mobile devices can be used to meet the urgency of learning need and can provide guidance and clues for learners in context‐aware environment.During mobile learning in context‐aware environment, students need to interact with mobile devices and environment.Mental effort reflects cognitive load related to interaction complexity, and it can affect learning performance.Self‐efficacy affects the degree of mental effort by affecting the firmness of performance goal.Cognitive load and learning performance are the main concerns that need to be considered when evaluating the effectiveness of mobile learning. What this paper adds: Interaction complexity has an impact on students' learning performance and mental effort in mobile learning.In terms of the three levels of interaction complexity in this study, the higher the interaction complexity is, the better the results that students will get in mobile learning.The higher the interaction complexity is, the higher the mental effort that students invested into in mobile learning would be.The medium self‐efficacy group invested the most mental effort and got the best grades, rather than the high or low group.Rule‐based mobile learning expert system is feasible and effective for students to learn by interacting with learning objects in context‐aware environment. Implications for practice and/or policy: Efforts should be made to make mobile learning systems more interactive; for example, learners can learn more effectively by interacting with mobile system, which can improve the frequency of observing learning objects.Rule‐based mobile learning expert system can give learners more opportunities to know how experts do when solving problems and can provide a potential method for students to learn in context‐aware environment. pubtype: Academic Journal doctype: algorithm consumer/patient teaching materials questionnaire/scale research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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