Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics

Fancsali, Stephen; Ritter, Steven

doi:10.1145/2567574.2567615

Cited by 19 publications

(11 citation statements)

References 5 publications

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“…Data that was collected using the Battleship Numberland educational game was used to learn about number line estimation skills among fourth-to sixth-grade students, as well as about the associations between level of difficulty and engagement (Lomas et al, 2011(Lomas et al, , 2013. Data logged from MATHia, an online software for middle-school mathematics, was analyzed for studying personalization of content based on students' personal interests, while also looking for associations between students' success and their personal tendency to have some personal fields of interest (Fancsali & Ritter, 2014). Data from other intelligent tutoring systems for mathematics was used to explore patterns of behavior in the system -e.g., help-seeking, multiple attempts, navigation -and their links with achievements and learning (Velasquez et al, 2014;Xie et al, 2017).…”

Section: Log-based Analysis Of Students' Learning In Mathematical Online Learning Environmentsmentioning

confidence: 99%

Students’ Activity in an Online Learning Environment for Mathematics: The Role of Thinking Levels

Haleva

Hershkovitz

Tabach

2020

Journal of Educational Computing Research

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Understanding students’ behavior while solving tasks at various levels is essential for the support educators may provide to students. The current study reports on a large-scale exploration of students' activity in an online learning environment for mathematics, while comparing between lower-order thinking (LOT) and higher-order thinking (HOT) applets, and between grade levels. We analyzed log files of N = 32,581 5th- and 6th-grade students from all over Israel (a full sample of users in the studied platform), specifically comparing scores, completion rates, completion times, and repetition levels in LOT and HOT applets. Using within-subject and between-subject t tests, we found that students' performance and completion rate on the LOT applets were overall higher than those of the HOT applets, which, combined with other findings, may point to meta-cognitive or motivational processes involved. We also point out to the high rates of students' manipulation of the system in a way that allow them to increase their score. Finally, we found that the various measures we used to characterize students' online activity are not necessarily strongly correlated with each other. These findings will help teachers to take informed decisions regarding the incorporation of digital learning environments in their classrooms.

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Section: Log-based Analysis Of Students' Learning In Mathematical Online Learning Environmentsmentioning

confidence: 99%

Students’ Activity in an Online Learning Environment for Mathematics: The Role of Thinking Levels

Haleva

Hershkovitz

Tabach

2020

Journal of Educational Computing Research

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“…There also might have been slight differences in the interventions themselves that contributed to this difference (e.g., differences in depth of problems or the wording of problems). Given that other recent studies of personalization have consistently shown no effect for "low-depth" personalization on immediate performance (e.g., Fancsali & Ritter, 2014;Høgheim & Reber, 2015, 2017Kosh, 2017), and few quantitative studies have examined higher depth interventions, it is still an open question whether context personalization can reliably impact short-term performance and accuracy.…”

Section: Effects Of Personalization On Performancementioning

confidence: 99%

“…This is problematic given that research on interest suggests it develops over time. There are a number of recent studies that have found no effect for personalization on performance or learning (Bates & Wiest, 2004; Cakir & Simsek, 2010; Høgheim & Reber, 2015; Ku & Sullivan, 2000; Simsek & Cakir, 2009), and one recent study that actually suggested a slight negative effect (Fancsali & Ritter, 2014). The studies that have found gains for learning have not examined motivational mechanisms as potential contributors, either omitting these measures entirely (Walkington, 2013), or simply showing pre-/postdifferences (Cordova & Lepper, 1996; Ku & Sullivan, 2000; López & Sullivan, 1992).…”

Section: Context Personalizationmentioning

confidence: 99%

The role of situational interest in personalized learning.

Bernacki¹,

Walkington²

2018

Journal of Educational Psychology

106

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Context personalization-the incorporation of students' out-of-school interests into learning tasks-has recently been shown to positively affect students' situational interest and their performance and learning in mathematics. However, few studies have shown effects on both interest and achievement, drawing into question whether context personalization interventions can achieve both ends. The effects of personalization are theorized to result from activation of students' prior knowledge of personal interests and generation of situational interest in math tasks, though theorists have begun to question whether situational interest serves as a mechanism by which learning outcomes are achieved. This experimental study examines whether personalizing 4 units of algebra problems that high school students (N ϭ 150) solve in an intelligent tutoring system could improve their performance in units (i.e., accuracy and learning efficiency) and on classroom exams, whether adolescents who solved personalized problems would report greater situational interest in units (and later, individual interest in math) than peers who solved standard problems, and whether paths through situational interest would contribute to effects of personalization on outcomes. High school students in the personalization condition reported greater triggered situational interest in experimental units, and triggered interest predicted in-tutor outcomes (accuracy, learning efficiency). A total effect of personalization was also observed on classroom exam performance and individual interest in mathematics. Implications for theories of interest and context personalization are discussed, as are implications for math instruction and design of personalized learning environments. Educational Impact and Implications StatementContext personalization refers to an instructional design strategy that incorporates students' out-of-school interests into learning tasks like math problems. Recent research has shown that personalization positively affects students' situational interest and their performance and learning in math, but students seldom obtain both outcomes. This study confirmed that personalizing 4 units of algebra story problems to students' out-of-school interests was sufficient to increase their situational interest in the task and to improve the efficiency with which they solved problems within the intelligent tutoring system. Months later, those who solved personalized problems also reported greater interest in mathematics and scored higher on a classroom math test than a control group. These results extend evidence for the benefits of personalization and confirm that personalizing problems to incorporate student interests at an appropriate depth and specificity can simultaneously produce effects on math interest and learning.

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“…Research suggests that although this approach may increase interest, it may not improve performance (Walkington et al 2015). Fancsali and Ritter (2014) actually report a slight negative effect for a personalization system of this type in the middle school intelligent tutoring curriculum MATHia. Walkington and Bernacki (accepted) refined their analysis and replicated the surprising slight negative effect.…”

Section: Review Of Research On Context Personalizationmentioning

confidence: 94%

“…A finer grain size may in some cases allow for better contextual grounding, but may also involve the incorporation of seductive details that distract from the underlying mathematics. Recent studies suggest that broader grain sizes of personalization also may not be particularly effective (Walkington et al 2016), and may actually be harmful to learning (Fancsali and Ritter 2014). However, approaches that have a fine grain size but low depth are also not particularly effective (e.g., Cakir and Simsek 2010;Høgheim and Reber 2015), while approaches with a medium grain size and depth show more promising results (Walkington 2013;Bernacki and Walkington, under review).…”

Section: Design Dimensions For Context Personalizationmentioning

confidence: 99%

Designing learning personalized to students’ interests: balancing rich experiences with mathematical goals

Walkington

Hayata

2017

ZDM Mathematics Education

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Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics

Cited by 19 publications

References 5 publications

Students’ Activity in an Online Learning Environment for Mathematics: The Role of Thinking Levels

Students’ Activity in an Online Learning Environment for Mathematics: The Role of Thinking Levels

The role of situational interest in personalized learning.

Designing learning personalized to students’ interests: balancing rich experiences with mathematical goals

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