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    Learning Analytics as a tool for supporting student wellbeing – Identifying student mental ill-health

    By Samantha Ahern, on 20 November 2017

    Research has shown that students who are distressed and at risk from mental ill-health will often exhibit one or more of the following indicators concurrently: academic struggles and failures, excessive absences from classes and obligations, excessive substance use, loneliness and isolation, social and interpersonal difficulties with others on campus, changes in self-care and lack of self-care, extreme risky behaviours, inability to tolerate frustration and normal stressors in college, inability to regulate emotions, hopelessness and despair(Anderson, 2015).

    Gemmill and Peterson(Gemmill and Peterson, 2006) have found that internet communication may have the same buffering effects of stressful life circumstances in the same way as non-internet communication by increasing measures of social support and perceived social support.

    This corresponds to findings by Gordon et al. (Gordon et al., 2007) who investigated types of student internet usage (meeting people, information seeking, distraction, coping and email) and four indicators of well-being: depression, social anxiety, loneliness and family cohesion.

    Their findings suggest that it is the type of internet usage, more so than the frequency of use that relates to depression, social anxiety and social cohesion. Using the internet for coping purposes was significantly associated with lower levels of family cohesion and higher levels of depression and social anxiety. Whereas, information seeking and email were positively associated with family cohesion.

    Research into predicting depression with social media(“Predicting Depression via Social Media – 6351”, n.d.), in this instance Twitter, found that social media contains useful signals for characterising the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered ego networks, heightened relational and medicinal concerns, and greater expression of religious involvement. It is noted that in order to identify changes in some behaviours, it was important to know the normal behaviours of the user e.g. an indicator of depression is a tendency to be more active at night. To be able to identify if there has been a change in activity the authors defined the normalised difference in number of postings made between the night window (9pm and 6am) and day window to be the “insomniac index” on a given day.

    In summary, these studies show that identifying behavioural changes are key to identifying student mental ill-health, this therefore implies that an understanding is needed of normative behaviours of students.

    References:

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