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TopDigital Learning Games And Student Support
The ability for digital learning games to increase student learning outcomes through gameplay is well documented in the literature (e.g., Shute, Rahimi et al., 2020; Clark et al., 2016; Mayer, 2019; Vogel, 2006; Wouters et al., 2013), as is the connection between a student’s affective state and their learning outcomes (Sabourin & Lester, 2014). A meta-analysis by Clark et al (2016) showed an overall positive effect for supports in learning games; however, the form these supports take can influence how effective they are. Adding supports to digital learning games can have (a) no impact (ter Vrugte et al., 2015), (b) an adverse impact (Adams & Clark, 2014), or (c) a positive impact (Wouters & Van Oostendorp, 2013) on outcomes. In the case of supporting players in digital learning games, design of the supports matters (Shute, Smith et al., 2020; Kuba et al., 2021; Ke, 2016; Wouters & van Oostendorp, 2013).
Previous examples of affective supports in digital learning environments mostly take the form of intelligent tutoring systems (ITSs). Some examples include: Affective AutoTutor, which monitors facial expressions and provides emotional scaffolds in response (D’Mello & Graesser, 2013), (Forbes-Riley & Litman, 2009; Forbes-Riley & Litman, 2011), and an affective learning companion, driven by physiological sensors, that encourages students to use metacognitive strategies in science modeling (VanLehn et al., 2014). These supports all rely upon complicated and/or expensive systems, such as facial recognition or lexical analysis. This research also may not apply to learning games, and the research into affective support in learning games is more limited (Sabourin & Lester, 2014).