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Applications of Analytics

Sentiment Analysis

Category: Diagnostic Analytics

Information about emotions (also known as ‘affect data’) can be identified in written text in social media or submissions from students (Medhat, et.al., 2014). A study found that “when using sentiment analyses of 51,000 student evaluation comments from 23 large OU modules, that substantial differences in lived and affective experiences could be identified” (Rientes & Jones, 2019: 114). Affect data can also be found in clickstream data, interaction patterns, and bodily signals (D’Mello, 2017:116-118). This technology is being commercialized; “With companies like Affectiva, BeyondVerbal and Sensay providing plug-and-play sentiment analysis software, the affective computing market is estimated to grow to $41 billion by 2022,” according to Harvard Business Review(Kleber, 2018).

Applications include instructional evaluation, institutional decision and policy-making, and learning system enhancement. (Dolianiti, et.al., 2019:413-414) For example, one project uses facial recognition technology to evaluate whether students in a classroom are bored. A facial recognition system being used in China reports on six types of behaviors ("reading, writing, hand raising, standing up, listening to the teacher, and leaning on the desk”) as well as identifying seven moods (“happy, upset, angry, fearful or disgusted”) logs both the behavior and the facial expressions (Jun, 2018). Affect data can also be used to inform the management of student discussions and in teacher evaluations (D’Mello, 2017:118).

Examples and Articles

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