Towards Predicting Future Transfer of Learning

Baker, Ryan S.; Gowda, Sujith M.; Corbett, Albert T.

doi:10.1007/978-3-642-21869-9_6

Cited by 31 publications

(16 citation statements)

References 13 publications

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“…While the current article uses a fine-grained assessment of learning, other results suggest that avoiding help is associated with reduced learning in the long term. For example, Baker, Gowda, and Corbett (2011) found evidence indicating that students who avoid help, and keep attempting and failing, do more poorly on later tests of transfer. However, Baker and colleagues looked across all skill levels, and a similar analysis found a similar trend also in the current study.…”

Section: Help Seeking On Low-skill Stepsmentioning

confidence: 99%

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

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Baker

Aleven

et al. 2014

Journal of the Learning Sciences

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Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the associations between specific help-seeking patterns and learning. We evaluate how students' help-seeking behaviors on each step in a tutored problem are associated with their success on subsequent steps that require the same skills. Analyzing learning at the skill level allows us to compare different help-seeking patterns within a single student, controlling for between-student variations. Overall, asking for help on challenging steps is associated with productive learning, and overusing help is associated with poorer learning. However, contrary to many help-seeking theories, avoiding help (and failing repeatedly) is associated with better

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Section: Help Seeking On Low-skill Stepsmentioning

confidence: 99%

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

Roll

Baker

Aleven

et al. 2014

Journal of the Learning Sciences

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“…In addition to this type of variables, in our study, we have made used of more complex variables such as for students' behaviour. Other similar works are based on an ITS for where they also try to predict learning outcomes (Baker et al, (); Baker, Gowda, & Corbett, (); Corbett, Kauffman, Maclaren, Wagner, & Jones, ()). There has been research on this ITS which reported that the developed detector needed only a limited amount of data (around the first 20% of a students' data) in order to predict with reasonably accuracy (Baker et al ); this is very interesting as it would allow to intervene in the early stages of a course.…”

Section: Related Workmentioning

confidence: 99%

“…Another important question that has been addressed in the field of prediction modeling on education is about which techniques or algorithms should be used. We can even find several papers which focus is to compare different techniques or variants of the same algorithm with the purpose of finding which one is the more effective to predict learning outcomes (Baker et al, ; Janecek & Haddawy, ; Koutina & Kermanidis, ). The research by Kotsiantis () performs a review of the different machine learning techniques for educational purposes.…”

Section: Related Workmentioning

confidence: 99%

“…We have used linear regression as other works do as well (Feng et al, (); Feng et al, (); Grafsgaard et al, (); Kelly et al, ()), because we expect a linear relationship between the selected variables and students' learning gains. Other authors used different methods such as Bayesian Knowledge Tracing model (Baker et al ), 1‐NN (Koutina & Kermanidis, ), neuronal networks such as radial basis functions (Delgado Calvo‐Flores et al ) or machine learning techniques (Mills et al ). Another interesting approach is to ensemble different prediction methods to achieve more robust results (Pardos et al ).…”

Section: Related Workmentioning

confidence: 99%

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Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators

2018

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One of the most investigated questions in education is to know which factors or variables affect learning. The prediction of learning outcomes can be used to act on students in order to improve their learning process. Several studies have addressed the prediction of learning outcomes in intelligent tutoring systems environments with intensive use of exercises, but few of them addressed this prediction in other web‐based environments with intensive use not only of exercises but also, for example, of videos. In addition, most works on prediction of learning outcomes are based on low level indicators such as number of accesses or time spent in resources. In this paper, we approach the prediction of learning gains in an educational experience using a local instance of Khan Academy platform with an intensive use of exercises and taking into account not only low level indicators but also higher level indicators such as students' behaviours. Our proposed regression model is able to predict 68% of the learning gains variability with the use of six variables related to the learning process. We discuss these results providing explanation of the influence of each variable in the model and comparing these results with other prediction models from other works.

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“…Steps have been taken toward detecting when learning occurs [4] and to predict how much of the acquired knowledge students can apply to other situations [5,6]. However, an obstacle in such research is how to gain an understanding of the user's cognitive or metacognitive processes while learning.…”

Section: Introductionmentioning

confidence: 99%

Using HCI Task Modeling Techniques to Measure How Deeply Students Model

Girard

Zhang

Hidalgo-Pontet

et al. 2013

Lecture Notes in Computer Science

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User modeling in AIED has been extended in the past decades to include affective and motivational aspects of learner's interaction in intelligent tutoring systems. An issue in such systems is researchers' ability to understand and detect students' cognitive and meta-cognitive processes while they learn. In order to study those factors, various detectors have been created that classify episodes in log data as gaming, high/low effort on task, robust learning, etc. When simulating students' learning processes in an ITS, a question remains as to how to create those detectors, and how reliable their simulation of the user's learning processes can be. In this article, we present our method for creating a detector of shallow modeling practices within a meta-tutor instructional system. The detector was defined using HCI (human-computer interaction) task modeling as well as a coding scheme defined by human coders from past users' screen recordings of software use. The detector produced classifications of student behavior that were highly similar to classifications produced by human coders with a kappa of .925.

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Towards Predicting Future Transfer of Learning

Cited by 31 publications

References 13 publications

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators

Using HCI Task Modeling Techniques to Measure How Deeply Students Model

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