Biological Lifestyle Factors Related To Cognition And Learning Performance In Adults In Distance Education
Author(s):
Conference:
ECER 2015
Format:
Paper

Session Information

22 SES 03 E, Innovative Perspectives on Teaching and Learning

Paper Session

Time:
2015-09-08
17:15-18:45
Room:
330. [Main]
Chair:
Diana Aguiar Vieira

Contribution

An important part of learning performance is influenced by individual characteristics. One of those are the environmental influences determined by lifestyle. We call these influences biological lifestyle factors (BLFs). Physical activity, sleep and nutrition are such BLFs and they contribute to brain health, cognitive performance and learning performance in traditional education in children and adolescents. Little research, however, has been conducted concerning these BLFs in adults participating in distance education, despite that this type of education is more and more in favor because of the freedom it offers that allows combining it with the demanding work life, family responsibilities (e.g., a partner and/or children) and the need for ongoing development in adult life. Knowledge on how lifestyle could contribute to learning performance in adults is highly desirable as it could increase the success of adults participating in distance education. Besides, advice on lifestyle changes are relatively cheap interventions of which benefits could be substantial (Ribeiro & Stickgold, 2014).

Maintaining a healthy lifestyle does not only result in a better physical and mental health state (Busch, Van Stel, Schrijvers, & de Leeuw, 2013), but also leads to better cognitive  performance (Small et al., 2006). Research indicates that a healthy lifestyle is beneficial for cognition and learning, as suggested by research in the domain of physical activity (PA) (e.g., Hillman, Kamijo, & Scudder, 2011), sleep (e.g., Dewald, Meijer, Oort, Kerkhof, & Bögels, 2010; Philip et al., 2012) and nutrition (e.g., Burkhalter & Hillman, 2011; Deliens, Clarys, De Bourdeaudhuij, & Deforche, 2013).

These examples provide the theoretical framework for the execution of this PhD project as PA, sleep and nutrition are all three factors that influence the body in a biological way. They exert influence on the regulation of the body’s physiological parameters and the subsequent processes. We will not go into detail on these underlying processes in this proposal because of briefness, but we refer to the following literature (cf., Gómez-Pinilla, 2008; van Praag, 2009; Winter et al., 2007; Wright et al., 2013). These, by lifestyle induced, physiological changes influence processes among which are synaptic pruning, synaptic plasticity and neuronal proliferation. These all contribute to brain plasticity, which is believed to be the basis for better cognitive and learning performance (e.g., Barenberg, Berse, & Dutke, 2011).

This led us to investigate the following main research question in an educational setting different than traditional education for children, namely adult distance education: What are the characteristics of adult distance education with respect to dropout and success?; What is the relation(s) between behavior on BLFs on the one hand and cognition and learning performance on the other? The objectives of the PhD project were: (1) to elaborate on the relation of the variables within each BLF with learning performance, but also combining these relations into one model predicting learning performance; (2) to elaborate on the relation of the variables within each BLF with cognitive performance, but also combining these relations into one model predicting cognitive performance; (3) to investigate whether cognitive performance was a mediator in the relation between the BLFs and learning performance.

The research proposed to be presented here, stems from a PhD project that will be finished at the time of the conference. The results from the first five, already available, empirical research articles will be presented in a concise but clear manner. One more research article is expected to follow in the future and will most likely be available at the conference and included in the paper presentation.

Method

The Adult Learning Open University Determinants (ALOUD) study conducted within this PhD project consisted of a longitudinal data collection in which all new students (N=4945) of the Open University of the Netherlands (OU) who were admitted between September 2012 and August 2013 were invited. The full design is described in our data paper (Neroni, Gijselaers, Kirschner, & de Groot, 2015). Via survey research, independent measures were collected within 9 weeks after invitation. These measures were the variables within the BLFs: physical activity, sedentary behaviour, chronotype, sleep duration, sleep quality, sleepiness, fatigue, caffeine, fish consumption, breakfast consumption and breakfast composition. The measured covariates were: age, sex, educational level, body mass index, study goal, study motive, study hours, working hours, computer abilities, nationality, native language, alcohol use, smoking, drug use and life satisfaction. The outcome measures were the cognitive variables: processing speed and two executive functions: updating and shifting. The cognitive measures were collected via digital online cognitive tests. The main outcome measure on learning performance was study progress, measured as completed courses over the first 14 months (i.e., the official time a registration for a course lasts at the OU). The educational model at the OU is modular, students can start whenever they want and with any possible study path they want. At first measurement, 2040 students fully responded. With these data, all analyses were executed. One article concerns a description of the full data sample with regard to covariates for successful, non-successful and non-starting students. Three articles concern the prediction of study progress for physical activity, sleep and nutrition, respectively. These articles employed a multiple regression technique with a negative binomial distribution (as the outcome measure was not normally distributed). The last article concerns the prediction of cognitive performance from all the BLFs. A structural equation methodology was used to execute path analysis, using a cross-validation technique.

Expected Outcomes

Summarized -in order of appearance per article- analyses revealed that: 1. There are many differences in the characteristics between successful, non successful and non starting students. 2. Physical and sedentary behavior were not predictive for study progress opposite to our expectations. 3. Chronotype did not predict study progress, in line with previous research (Horzum, Önder, & Beşoluk, 2014; Jovanovski & Bassili, 2007; Önder, Horzum, & Beşoluk, 2011), sleep duration and sleep quality was not predictive for study progress, opposite to our expectation. 4. The consumption of fish, caffeine and breakfast did not predict study progress, in line with our hypotheses. 5. Sedentary behavior was predictive for cognitive performance. Physical activity, chronotype, sleep duration, sleep quality, caffeine, fish consumption and breakfast consumption were not predictive for cognitive performance. These results will be extensively discussed during the paper presentation. Two more articles will be published in the coming months, when the PhD project is finalized. These results will also be included in the paper presentation.

References

Barenberg, J.,Berse, T.,& Dutke, S. (2011). Executive functions in learning processes: Do they benefit from physical activity? Educational Research Review, 6(3), 208–222. Burkhalter, T.M.,& Hillman, C.H. (2011). A narrative review of physical activity, nutrition, and obesity to cognition and scholastic performance across the human lifespan. Advances in Nutrition, 2(2), 201S–6S. Busch, V.,Van Stel, H.F.,Schrijvers, A.J.P.,& de Leeuw, J.R.J. (2013). Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: A cross-sectional study. BMC Public Health, 13, 1118. Deliens, T.,Clarys, P.,De Bourdeaudhuij, I.,& Deforche, B. (2013). Weight, socio-demographics, and health behaviour related correlates of academic performance in first year university students. Nutrition Journal, 12, 162. Dewald, J.F.,Meijer, A.M.,Oort, F.J.,Kerkhof, G.A.,& Bögels, S.M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review. Sleep Medicine Reviews, 14(3), 179–89. Gómez-Pinilla, F. (2008). Brain foods: The effects of nutrients on brain function. Nature Reviews. Neuroscience, 9(7), 568–78. Hillman, C.H.,Kamijo, K.,& Scudder, M. (2011). A review of chronic and acute physical activity participation on neuroelectric measures of brain health and cognition during childhood. Preventive Medicine, 52, S21–8. Horzum, M.B.,Önder, İ.,& Beşoluk, Ş. (2014). Chronotype and academic achievement among online learning students. Learning and Individual Differences, 30, 106–111. Jovanovski, D.,& Bassili, J.N. (2007). The relationship between morningness - eveningness preference and online learning. Biological Rhythm Research, 38(5), 355–365. Neroni, J., Gijselaers, H. J. M., Kirschner, P. A., & de Groot, R. H. M. (2015). The Adult Learning Open University Determinants (ALOUD) study: Biological and psychological factors associated with learning performance in adult distance education. British Journal of Educational Technology, Early view. http://doi.org/10.1111/bjet.12288 Önder, İ.,Horzum, M.B.,& Beşoluk, Ş. (2011). Chronotype, learning approach, type/time of instruction and academic achievement of the university students. Circadian Rhythms (pp. 161–186). Nova Science Publishers, Inc. Philip, P.,Sagaspe, P.,Prague, M.,Tassi, P.,Capelli, A.,Bioulac, B.,… Taillard, J. (2012). Acute versus chronic partial sleep deprivation in middle-aged people: differential effect on performance and sleepiness. Sleep, 35(7), 997–1002. Ribeiro, S.,& Stickgold, R. (2014). Sleep and school education. Trends in Neuroscience and Education, 3(1), 18–23. Small, G.W.,Silverman, D.H.S.,Siddarth, P.,Ercoli, L.M.,Miller, K.J.,Lavretsky, H., … Phelps, M. E. (2006). Effects of a 14-day healthy longevity lifestyle program on cognition and brain function. The American Journal of Geriatric Psychiatry, 14(6), 538–45. Taylor, D.J. http://doi.org/10.1080/15402002.2011.602776 Van Praag, H. http://doi.org/10.1016/j.tins.2008.12.007 Winter, B. http://doi.org/10.1016/j.nlm.2006.11.003 Wright, K.P. http://doi.org/10.1016/j.cub.2013.06.039

Author Information

Jérôme Gijselaers (presenting / submitting)
Open University of the Netherlands
Heerlen
Open University of the Netherlands; Maastricht University, the Netherlands
Open University of the Netherlands

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