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The current generation of university students has witnessed the extension of ICT into every aspect of their life. Other conditions kept constant, various personalized learning tools, such as smartphones and other e-learning platforms are enabling students to engage with academic and social activities associated with the classroom much differently than before (Huang, Teo & Schere, 2020). Personalised ICT tools can be used by students to remotely access educational materials and maintain social relations with fellow students and teachers, thereby facilitating Behavioural, affective and cognitive student engagement (Bond et al., 2020). However, usage of ICT and positive and negative consequences accruing from it is largely contingent on many underlying factors (Hargittai & Hinnant, 2008). In this context, this study tries to analyze the influence of personal factor such as alienation on students ICT use. While several studies have analysed the relationship of SAL with ICT in digitally-enabled classrooms (Li, et al., 2017), an analysis is required to elucidate such a relationship in conventional educational settings, particularly on personalized use orientations (Bond et al., 2020). The findings emphasize that not only providing technological infrastructure and equipment but student’s use of ICT should be critically considered in the policies of ICT in education (Bergdahl et al., 2018; Howard, Ma & Yang, 2016; Popenici, 2013).
Existing perspectives on e-learning tend to ignore the importance of individual differences in ICT usage besides other underlying factors shaping such differences (Dar & Jan 2019). Research has explicated the role of psychological, social and cultural factors in producing individual differences in preferences, styles and motivation of ICT use (Kennedy, et al., 2010). In today’s technologically personalized learning environments asking for mere access to digital equipment will not serve our ends, there is a need to look at the consequence that differential use has, thereby highlighting the complex nature of technological adoption in educational settings (Selwyn, 2004). Moreover, verifying the consequences of differential use of digital technologies may help to understand the larger social and educational implications of the second level of the digital divide (Hargittai & Hinnant, 2008). Different varieties of ICT use puts people at a differential advantage in terms of the benefits they accrue from it. For example, some scholars have tried to distinguish between different ICT activities based on their potential in enhancing one’s life chances. Capital-enhancing ICT use is more likely to offer users opportunities for upward mobility and instrumentality than certain other types of online activities (e.g., checking sports scores, reading jokes) (see DiMaggio and Hargittai, 2001). No doubt, income can directly influence the availability of physical access & attitudes for digital use (Warschauer 2002; Hassani, 2006). But, income does not completely explain the process of technological capital. Therefore, understanding the consequences of differential usage in terms of their relationship with the SAL helps to understand a new form of technologically mediated social exclusion (McKenzie, 2007). An individual's learning in a personalized ICT based environment needs to be seriously considered, as many factors may unwittingly put certain sections of students at a relative disadvantage than others. That will demand policies for educating students about the effective use of personalized learning environments.