COULD WORKING MEMORY PREDICT PERFORMANCE IN DIFFERENT STUDY TASKS?
University of Economics in Bratislava (SLOVAKIA)
About this paper:
Appears in: EDULEARN14 Proceedings
Publication year: 2014
Conference name: 6th International Conference on Education and New Learning Technologies
Dates: 7-9 July, 2014
Location: Barcelona, Spain
Abstract:When solving many problems we face in a real life, we often have to rely on clues with various predictive validities for correct solution. We constructed such task and examined how well students remembered the most predictive clues and were able to use them successfully at later time. We were also interested whether success in this task would be related to successful learning of other various tasks necessary for passing a course of Managerial Informatics at University of Economics.
75 students of management from University of Economics attending course of Managerial Informatics were asked at the beginning of a school term to rate business plan presented as a table of 25 characteristics (existing competition, price, profitability, etc.). After four weeks they were presented with these 25 characteristics along with their predictive validity (number from 0 to 1) for 3 minutes again and were asked to remember as many characteristics with their predictive validity as possible. Immediately after presentation, they were asked to write down all information they remembered (characteristics with their respective predictive validity). Students remembered in average 6.64 (SD = 2.99) characteristics, but only 3.54 (SD = 1.88) in average were correct. Then we analysed how the number of properly remembered characteristics correlates with five other tasks that students had to pass for credit
The tasks and correlation with properly remembered were as follows. Two tasks measured ability to understand and learn to use accounting software, Alpha (r = .27; p =.019) and Omega (r = .256; p = .027) from Kros Corporation. Third task tested the ability to search in internet, compare data and answer 30 questions about ERP software (r = .286; p = .013). Fourth task was a case study of a real specific company and students had to analyse its IT status, explain the causes and bring solutions for its improving (r = .023; p = .847). For all these tasks number of properly remembered characteristics could be used as a significant predictor of successful solution of problems. Last task was to memorize data for final exam in Managerial Informatics (r = .085; p = .471). We also examined relation of amout remembered cues to cognitive style measured by PID inventory subscales but relationship was insignificant: for both PID deliberation scale (r = .046; p = .695) and PID intuition scale (r = -.035; p = .766).
According to number of properly remembered cues students were assigned to three groups: perfect memory (1 standard deviation above mean), poor memory (1 standard deviation below mean) and all others who were in range between standard deviation. There were no significant differences between these groups in task solving, except ERP task where group perfect memory had best score in ERP and all groups differ significantly F(2) = 3.686; p = .03.
Our results contradicted our expectations. Ability to remember practical cues for correctly assessing business plan was expected to be in strongest relationship with the task, where only memory was necessary to get top performance – memorising for final exam. This assumption was not supported by the results. Although learning to use new software and searching for information about ERP software was not expected to be related with working memory, our results proved other way. It seems that practical working memory performance has stronger effect on different learning tasks than originally expected.
Keywords: Working memory, study outcome, managerial informatics.