Understanding the underpinnings of complex problem solving in a higher-education setting.

CASCALLAR, E. C.; GONZALEZ CAINO, P.; GREIFF, S.; MUSSO, M. F.; MUSTAFIC, M.

Tampere

Conferencia; 17th Biennial conference of the European Association for Research on Learning and Instruction (EARLI); 2017

European Association for Research on Learning and Instruction (EARLI)

In recent years, educational and cognitive research has emphasized the importance of cross-curricular skills such as complex problem solving (CPS) not only for success in the learning process, but also for success in current modernsocieties requiring these complex skills. This study utilized a machine-learning approach to develop models predicting the performance of first-year university students in several dimensions of CPS, examining the contribution of complex interactions of working memory, attention, perseverance, openness and background. Once these models were established,accurately describing each of the performance groups in three aspects of CPS (exploration strategies, knowledge acquisition phase, and knowledge application phase), differences between low and high performers in these CPS phases where studied for several variables which have been shown to have important effects in performance: cognitive (working memory, executive attention, operations reaction time, executive attention reaction time), motivational and self-regulation(perseverance, openness, subjective competence, task attraction), and background variables (levels of education of mother and father, SES indicators, and others). Results indicate specific effects that show the pattern of mediation of these various variables on CPS and its three aspects considered in the study. The contribution of the findings relate to cognitive theoryand to possible educational interventions that take into account individual differences in approaching complex problem situations.