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Evaluating Utilization of Big Data for Learning and Assessment of Mathematics in Nairobi County Secondary Schools, Kenya

The efficient use of Big Data in assessment for learning is a game changer in the teaching- learning (TL) process. Teachers, students, educationists and other stakeholders on the day-to-day basis , interact, generating enormous (big) data used in the TL as well as in assessment cycles. These stakeholders should with ease and precision, make informed evaluation of instructional and assessment programmes. However, statistics show that achievements level in secondary education, especially in mathematics is below expectations yet a lot of big data have continued to be generated. This paper endeavours to highlight the relationship between the performance of Students in secondary schools in mathematics and the access as well as the use of big data in mathematics during the TL process. The evaluation questions are: to what extent do teachers, and Students access big mathematical data for assessment? Do the teachers and Students have requisite skills to validly and reliably construct, analyse and interpret instructional and assessment content. Prescriptive Instructional Design (ADDIE model), based on the Cognitive Learning Theory guided the study. The Naturalistic Research Design and the Case Study methodology were used. The research data was collected using semi-structured interviews on focused groups; and analysed using the coding, document analysis as well as the descriptive statistics. The study findings revealed that learners’ performance was proportionate to extend of their access and use of big mathematical data. The study also observed that the validity and reliability of instructional and assessment data given to the Students was affected by teachers’ inadequate skills in test construction, assessment, storing and retrieval of the relevant big data. It is recommended that teachers and Students be capacity built to make use of big instructional and assessment data for effective learning and assessment.

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