Learning assessment and depreciation in learning

Hae Lim Seo, Purdue University

Abstract

The learning curve model was initially proposed by Wright [1936]. Ever since its introduction, numerous theoretical and empirical studies have been conducted on the learning effect, and many new concepts associated with learning have been developed. However, there are still some areas that are not clear. The purpose of this study is two fold: First, we clarify those unclear areas in learning so that the correct models can be applied in implementing the learning analysis. Second, we propose a new model to explain the depreciation phenomenon of accumulated knowledge. Specifically, we examine the known controversial issue of unit learning vs. cumulative average learning models. It has been observed that the cumulative average learning model falsely shows a statistically significant learning effect even for a randomly generated dataset. We also discuss the issue of using aggregate vs. disaggregated learning models. The aggregate learning model is not as accurate in learning assessment as the disaggregated model, and it may cover up some information that is useful for an effective management. We examine one of the newly developed concepts associated with learning, which is known as depreciation. We first discuss the potential problems of the existing depreciation model proposed by Argote et al. [1990]. Then, a new learning depreciation model is proposed, empirically tested, and compared with the Argote model and with the conventional learning model. Finally, the optimal investment on preventing depreciation and inducing learning is discussed.

Degree

Ph.D.

Advisors

Plante, Purdue University.

Subject Area

Management

COinS