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May 29 2019 - 11:40 AM
What is the Effect of Biases on Self-Directed Learning?

When it comes to self-directed learning (SDL), learners themselves decide which information and skills to acquire. They base their decisions off on the hypothesis created by them. This process is known as hypothesis generation, which is defined as the “formation of candidate explanations or models of a target domain, which then guide reasoning, prediction, and exploration” (Markant, 2018, p. 1). However, this process may be biased due to myriad factors such as perceiving information through one’s own cultural lens, inability to consider a different point of view, prior knowledge and experience, cognitive biases, and more (Markant, 2018). 

Markant (2018) conducted a study to investigate how biased hypothesis generation influences the self-directed category learning. Category learning is defined as learning category rules or structures and establishing category boundaries (Markant, 2018). Consequently, this facilitates concept learning and schema construction. Nonetheless, the study consisted of two experiments: perceptual and abstract category learning tasks (Markant, 2018). In both experiments, the participants were supposed to classify items of their choice based on their features into categories, which, essentially, leads to learning of one-dimensional (1D) and two-dimensional (2D) rules (Markant, 2018). This means that the participants have to learn the category boundaries. Markant (2018) found that biases have an influence on the participants’ ability to learn the categorical relationship even though they selected these items. This explains why SDL has produced mixed results in educational settings (Markant, 2018). The results of this study imply that in classes, where SDL is employed, the students select the learning content and the learning techniques based on the biases mentioned above. Furthermore, they choose the content based on their perception of needed skills and information. Therefore, it might be difficult for the instructors to assess the students’ knowledge and skill competencies and create a rubric. This raises several following questions: 1) What measures should be taken in order to reduce the effect of these biases? 2) To what degree should SDL be implemented in classrooms? 3) How instructors balance their guidance and SDL in educational institutions?    



Markant, D. B. (2018). Effects of biased hypothesis generation on self-directed category learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, doi:10.1037/xlm0000671

Posted in: Research|By: Anna Lizarov|1238 Reads