A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy

Mathematics competency is fast becoming an essential requirement in ever greater parts of day-to-day work and life. Thus, creating strategies for improving mathematics learning in students is a major goal of education research. However, doing so requires an ability to look at many aspects of mathematics learning, such as demographics and psychological dispositions, in an integrated way as part of the same system. Large-scale assessments such as the Programme for International Student Assessment (PISA) provide an accessible and large volume of coherent data, and this gives researchers the opportunity to employ data-driven approaches to gain an overview of the system. For these reasons, we have used machine learning to explore the relationships between psychological dispositions and mathematical literacy in Australian 15-year-olds using the PISA 2012 data set. Our results from this strongly data-driven approach re-affirm the primacy of mathematics self-efficacy and highlight novel complex interactions between mathematics self-efficacy, mathematics anxiety and socio-economic status. In this paper, we demonstrate how education researchers can usefully employ data-driven modelling techniques to find complex non-linear relationships and novel interactions in a multidimensional data set.     

Does a resistance exercise session with continuous or intermittent blood flow restriction promote muscle damage and increase oxidative stress?

The aim of this study was to compare the effect of low-load resistance exercise (LLRE) with continuous and intermittent blood flow restriction (BFR) on the creatine kinase (CK), lactate dehydrogenase (LDH), protein carbonyl (PC), thiobarbituric acid-reactive substance (TBARS) and uric acid (UA) levels in military men. The study included 10 recreationally trained men aged 19 ± 0.82 years who underwent the following experimental protocols in random order on separate days (72–96 h): 4 LLRE sessions at a 20% 1RM (one-repetition maximum [1RM]) with continuous BFR (LLRE + CBFR); 4 LLRE sessions at 20% 1RM with intermittent BFR (LLRE + IBFR) and 4 high-intensity resistance exercise (HIRE) sessions at 80% 1RM. The CK and LDH (markers of muscle damage) levels were measured before exercise (BE), 24 h post-exercise and 48 h post-exercise, and the PC, TBARS and UA (markers of oxidative stress) levels were measured BE and immediately after each exercise session. There was a significant increase in CK in the HIRE 24 post-exercise samples compared with the LLRE + CBFR and LLRE + IBFR (P = 0.035, P = 0.036, respectively), as well as between HIRE 48 post-exercise and LLRE + CBFR (P = 0.049). Additionally, there was a significant increase in CK in the LLRE + CBFR samples BE and immediately after each exercise (Δ = 21.9%) and in the HIRE samples BE and immediately after each exercise, BE and 24 post-exercise, and BE and 48 post-exercise (Δ values of 35%, 177.6%, and 177.6%, respectively). However, there were no significant changes in LDH, PC, TBARS, and UA between the protocols (P > 0.05). Therefore, a physical exercise session with continuous or intermittent BFR did not promote muscle damage; moreover, neither protocol seemed to affect the oxidative stress markers.