Making sense of learner and learning Big Data: reviewing five years of Data Wrangling at the Open University UK

Most distance learning institutions collect vast amounts of learning data. Making sense of this ‘Big Data’ can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated in 2012 a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical and sense-making advice to staff and senior management. Given substantial changes within the OU (e.g. new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case study provides an in-depth review of lessons learned of five years of data wrangling. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.     

Learning consequences for university students using computerized mastery testing

Mastery learning, it is claimed, is among the most successful practices in higher education. Evidence supporting this claim has recently been criticized as empirically weak. This experiment employed a four-group, pre-post design to examine the learning consequences of a computer-administered mastery-testing approach in comparison with a seminar-discussion approach in a third-year university course. The mastery-testing approach generated significantly higher learning achievement than did the seminar-discussion approach. Replication and extension of this research approach are recommended to support generalizations across mastery-testing techniques, delivery systems, and disciplines.     

Erratum to: Effect of Current Electricity Simulation Supported Learning on the Conceptual Understanding of Elementary and Secondary Teachers

This study examined the impact of computer simulation and supported science learning on a teacher’s understanding and conceptual knowledge of current electricity. Pre/Post tests were used to measure the teachers’ concept attainment. Overall, there was a significant and large knowledge difference effect from Pre to Post test. Two interesting interactions were observed during the data analysis. The first was the difference between Elementary and Secondary teachers. Both groups had significant gains, with large effect sizes, but the Elementary teachers (Pre-Mean = 3.70, Post-Mean = 7.51) started lower and ended higher exhibiting a significantly larger gain than the Secondary teachers (Pre-Mean = 4.96, Post-Mean = 6.71). The second interaction was the impact of gender. Both groups showed significant gains, with large effect sizes, but females (Pre-Mean = 3.90, Post-Mean = 7.21) gained significantly more than males (Pre-Mean = 5.13, Post-Mean = 7.01). These results confirm that computer simulation supported science learning can have a positive effect on concept attainment in teachers.     

Students’ communicative behavior adaptability in CSCL environments

By the proliferation of online courses, the social dimension of computer supported collaborative learning (CSCL) is becoming more important than before. Research shows that communicative behavior adaptation to the computer medium is a critical issue in CSCL social relationship development. Two dominant theories in the CSCL field, social information processing theory and adaptive structuration theory, argue that individuals do not simply receive the technology passively but they adapt their behavior to increase benefits from the technology. This paper develops an instrument in order to operationalize the notion of individual’s communicative behavior adaptability in CSCL. Through an exploratory factor analysis performed on a small sample of post graduate students of an online degree in an Australian university, three factors have been unveiled: (1) individual perception of self-representation, (2) individual perception of compatibility, and (3) individual perception of the use of computer technology. Identification of these factors is expected to facilitate understanding of individuals’ social behaviors in CSCL environment, which in turn will guide the design of CSCL systems. In addition, the paper examines the relationships between the extracted factors and four environmental factors: learner’s characteristics, course characteristics, instructor characteristics, and technology characteristics. The results show that none of these characteristics strongly affect perception of self-representation or perception of the use of computer technology. On the other hand, a strong relationship was found between perception of compatibility and learner’s and course characteristics. The reliability as well as validity of the study is examined and findings are discussed. These findings will provide further insights into the design process of CSCL systems.