In this study, which is part of a larger one, we explored the effect of integrating question-asking activities and providing answers to these questions while reading historical mathematical texts on the self-reported cognitive load by prospective mathematics teachers. For that matter, we conducted a study in a two-semester course dealing with historical mathematics texts from ancient eras. The research group was divided into two groups (experimental and control) to detect the effect of question-asking. We focused on data gathered from three tasks: the first task concerned the Egyptian algorithm dealing with the multiplication of two natural numbers. The second task concerned the Babylonian algorithm dealing with solving two equations with two unknown variables using geometrical considerations. The third task concerned a Vedic algorithm dealing with the squaring of two-digit numbers. The experiment group was instructed to ask questions while coping with the texts and answer them, while the control group received no special instructions. During the research on participants' attempts to decipher the algorithms, they were asked to think aloud and audio record their ideas and thoughts. In addition, they were asked to address a self-esteem index questionnaire in which they had to report on the level of difficulty they experienced during their engagement in the tasks right after coping with each task and provide justifications to their ranking. The questionnaire included statements referring to extraneous and intrinsic cognitive load. The present study focuses merely on results obtained from a self-esteem index questionnaire. The data was analysed using quantitative and qualitative methods. Three main results were obtained: First, the reported cognitive load by the control group remained almost the same along the three tasks. Second, a moderate decrease was observed regarding cognitive load among the experimental group who implemented question-asking. Third, question-asking was found to support the decrease of cognitive load up to a specific limit, depending on the gap between existing knowledge and new information.
© 2001-2024 Fundación Dialnet · Todos los derechos reservados