Technology Affinity, Interaction, Accessibility, Attitude to Learning, and Learning Performance: Examining the Interrelationships

There is unanimity among educational researchers regarding the causal effects of the use of technologies in education. However, issues concerning how human-technology attributes such as technology affinity, interaction, channel affinity and accessibility are related to learning remain unclear. This study examined how the levels of students’ technology affinity, interaction, and accessibility are related to the students’ attitudes to learning and learning performance. The study deployed the basic descriptive survey design. Five hundred and thirty-four students drawn from six Open and Distance Learning (ODL) universities located in three of the six States in South West, Nigeria participated. Data sources included four questionnaires and a 15-item Technology-mediated Scientific Cognitive Abilities Test. Data were analysed using descriptive statistics, correlation analysis and regression analysis. The study found that technology affinity levels exhibited a statistically significant but negative relationship with attitude to learning (β = -0.21, t = -3.69, p = 0.0000), while the relationship with learning performance was statistically non-significant but positive (β = -0.21, t = -3.69, p = 0.692). Interaction levels exhibited a non-significant and positive relationship with attitude to learning (β = 0.05, t = 0.99, p = 0.322), and a statistically non-significant but negative relationship with learning performance (β = -0.10, t = -1.40, p = 0.161). Accessibility was significantly but negatively related to attitude to learning (β = -0.13, t = -2.242, p = 0.025, intercept = -0.88) while exhibiting a non-significant but positive relationship with learning performance (β = 0.05, t = 0.68, p = 0.499, intercept = 0.25). The study concluded that significant relationships existed among and between the ODL students’ technology attributes, attitude to learning and learning performance. It was recommended, among other things, that in adopting digital technologies for educational purposes, higher education institutions (HEIs) should sufficiently factor in the impact of the students’ technology attributes.