An Explanatory Item Response Theory Approach for a Computer-Based Case Simulation Test

Purpose The purpose of this paper is to evaluate the effect of design awareness on consumers’ purchase intention. Design/methodology/approach The experiment consisted of showing a new beer package design to 185 participants who evaluated it using a self‐administered questionnaire. Findings Using an Item Response Theory approach, results show that there are two dimensions of consumer design awareness: basic design and differential design. These findings are, to some extent, consistent with the theoretical discussion within design literature. Moreover, a multiple regression model estimates the effect of both dimensions on consumers’ purchase intention, and the paper concludes that both dimensions have a similar effect (p<0.05). The sign of the effects are consistent with the theoretical discussion. Practical implications The design of new products must consider attributes associated to the basic and practical use of a product as well as those attributes that mark a comparative difference in the product category. Originality/value This paper conceptually and empirically combines two different areas of knowledge (design and consumer behavior) under the design awareness construct. This concept evaluates consumers’ perceptions about new products, facilitating more accurate decisions in cases of innovation.

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

Information Discovery and Delivery ◽

10.1108/idd-08-2018-0030 ◽

2019 ◽

Vol 47 (2) ◽

pp. 67-75 ◽

Cited By ~ 1

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.

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