Context Personalization, Preferences, and Performance in an Intelligent Tutoring System for Middle School Mathematics (Short paper, LAK14)

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

Abstract: Learners often think math is unrelated to their own interests. Instructional software has the potential to provide personalized instruction that responds to individuals’ interests. Carnegie Learning’s MATHia software for middle school mathematics asks learners to specify domains of their interest (e.g., sports & fitness, arts & music), as well as names of friends/classmates, and uses this information to both choose and personalize word problems for individual learners. Our analysis of MATHia’s relatively coarse-grained personalization contrasts with more fine-grained analysis in previous research on word problems in the Cognitive Tutor (e.g., finding effects on performance in parts of problems that depend on more difficult skills), and we explore associations of aggregate preference “honoring” with learner performance. To do so, we define a notion of “strong” learner interest area preferences and find that honoring such preferences has a small negative association with performance. However, learners that both merely express preferences (either interest area preferences or setting names of friends/classmates), and those that express strong preferences, tend to perform in ways that are associated with better learning compared to learners that do not express such preferences. We consider several explanations of these findings and suggest important topics for future research.

Overview by the Hub: This study investigates whether personalising maths questions using names of learners friends impacts learning (names provided by learner). They found that honouring the preference given by the learner had mixed results. It is speculated that there may be a ‘conciensous’ factor in operation – that those who set preferences are generally more concisous learners. The authors conclude by suggesting that the personalisation may need to be made more clear to learners or done more frequently (so they appreciate it) but also ‘that the use of personalisation features appears to be associated with improved outcomes.’

 

Citation: Fancsali, S. and Ritter, S. (2014) Context Personalization, Preferences, and Performance in an Intelligent Tutoring System for Middle School Mathematics. The 4th International Conference on Learning Analytics and Knowledge. March 24-28, 2014. Indianapolis, USA. http://lak14indy.wordpress.com/lak-2014-conference-program-schedule/

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