Effects of Feedback in a Computer-Based Learning Environment on Students’ Learning Outcomes

2015 ◽  
Vol 85 (4) ◽  
pp. 475-511 ◽  
Author(s):  
Fabienne M. Van der Kleij ◽  
Remco C. W. Feskens ◽  
Theo J. H. M. Eggen
Keyword(s):  
Learning Environment ◽  
Learning Outcomes ◽  
Computer Based Learning ◽  
Computer Based ◽  
Effects Of Feedback

The effectiveness of methods for providing written feedback through a computer-based assessment for learning: a systematic review

CADMO ◽  
2011 ◽  
pp. 21-38
Author(s):  
Fabienne M. van der Kleij ◽  
Caroline F. Timmers ◽  
Theo J.H.M. Eggen
Keyword(s):  
Learning Outcomes ◽  
Lower Order ◽  
Assessment For Learning ◽  
Future Research ◽  
Written Feedback ◽  
High Quality Research ◽  
Computer Based ◽  
Higher Order Learning ◽  
Effects Of Feedback ◽  
Knowledge Of Correct Response

This study reviews literature regarding the effectiveness of different methods for providing written feedback through a computer-based assessment for learning. In analysing the results, a distinction is made between lower-order and higherorder learning. What little high-quality research is available suggests that students could benefit from knowledge of correct response (KCR) to obtain lower-order learning outcomes. As well, elaborated feedback (EF) seems beneficial for gaining both lower-order and higher-order learning outcomes. Furthermore, this study shows that a number of variables should be taken into account when investigating the effects of feedback on learning outcomes. Implications for future research are discussed.

Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill

2019 ◽  
Vol 47 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Youngjin Lee
Keyword(s):  
Data Mining ◽  
Item Response Theory ◽  
Problem Solving ◽  
Learning Environment ◽  
Item Response ◽  
Predictive Power ◽  
Response Theory ◽  
Content Type ◽  
Computer Based Learning ◽  
Computer Based

Purpose The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment. Design/methodology/approach Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve. Findings The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems. Originality/value Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

Sign in / Sign up

Export Citation Format

Share Document