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Formative assessment generates feedback on students' performance, thereby accelerating and improving student learning. Anecdotal evidence gathered by a number of evaluations has hypothesised that audio feedback may be capable of enhancing student learning more than other approaches. In this paper we report on the preliminary findings of a quasi‐experimental study employing qualitative techniques for triangulation, conducted to evaluate the efficacy of formative audio feedback on student learning. We focus on the delivery of 'voice emails' to undergraduate students (n = 24) and evaluate the efficacy of such feedback in formative assessment and ergo students' learning, as well as achieving a better understanding of students' feedback behaviour post‐delivery. The results indicate that audio feedback better conforms to existing models of 'quality' formative feedback, can enhance the student learning experience and can be more efficient in feedback delivery. Despite this, and high levels of feedback re‐use by student participants, the audio treatment group underperformed in learning tasks when compared with the control group. Differences between the groups were not statistically significant and analyses of individual and mean learning gains across the treatment group provide little indication of improvements in learning.

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Data Mining uses tools such as statistical models, machine learning, and visualization to "Mine" (extract) the useful data and patterns from the Big Data, whereas Big Data processes high-volume and high-velocity data, which is challenging to do in older databases and analysis program.

Data Mining uses tools such as statistical models, machine learning, and visualization to "Mine" (extract) the useful data and patterns from the Big Data, whereas Big Data processes high-volume and high-velocity data, which is challenging to do in older databases and analysis program.

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Wiley InterScience :: JOURNALS :: Psychological Science

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