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Roschelle, Jeremy; Feng, Mingyu; Murphy, Robert F.; Mason, Craig A. – AERA Open, 2016
In a randomized field trial with 2,850 seventh-grade mathematics students, we evaluated whether an educational technology intervention increased mathematics learning. Assigning homework is common yet sometimes controversial. Building on prior research on formative assessment and adaptive teaching, we predicted that combining an online homework…
Descriptors: Middle School Students, Grade 7, Secondary School Mathematics, Mathematics Achievement
Chen, Lujie; Li, Xin; Xia, Zhuyun; Song, Zhanmei; Morency, Louis-Philippe; Dubrawski, Artur – International Educational Data Mining Society, 2016
Solving challenging math problems often invites a child to ride an "emotional roller-coaster" and experience a complex mixture of emotions including confusion, frustration, joy, and surprise. Early exposure to this type of "hard fun" may stimulate child's interest and curiosity of mathematics and nurture life long skills such…
Descriptors: Young Children, Mathematics Education, Problem Solving, Psychological Patterns
Duval, Erik, Ed.; Sharples, Mike, Ed.; Sutherland, Rosamund, Ed. – Springer, 2017
This book gives an overview of the state-of-the-art in Technology Enhanced Learning (TEL). It is organized as a collection of 14 research themes, each introduced by leading experts and including references to the most relevant literature on the theme of each cluster. Additionally, each chapter discusses four seminal papers on the theme with expert…
Descriptors: Educational Technology, Technology Uses in Education, Educational Research, Learning Theories
Liu, Ran; Koedinger, Kenneth R. K – International Educational Data Mining Society, 2017
Research in Educational Data Mining could benefit from greater efforts to ensure that models yield reliable, valid, and interpretable parameter estimates. These efforts have especially been lacking for individualized student-parameter models. We collected two datasets from a sizable student population with excellent "depth" -- that is,…
Descriptors: Data Analysis, Intelligent Tutoring Systems, Bayesian Statistics, Pretests Posttests
Koedinger, Kenneth R.; McLaughlin, Elizabeth A.; Stamper, John C. – International Educational Data Mining Society, 2012
Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational…
Descriptors: Educational Technology, Intelligent Tutoring Systems, Educational Improvement, Mathematics
Eagle, Michael; Johnson, Matthew; Barnes, Tiffany – International Educational Data Mining Society, 2012
We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…
Descriptors: Data Analysis, Interaction, Network Analysis, Problem Solving
Wu, Ejean; Yang, Shu Ching – Computer Assisted Language Learning, 2016
This study examines the differential impact of tutor labeling vs. non-labeling approaches on the performance; motivation beliefs; and cognitive, social, and teaching presence of low-achieving students. Two interactive tutoring strategy patterns are investigated based on the taxonomical e-moderating model of Salmon. In addition, the tutees' online…
Descriptors: Low Achievement, Student Behavior, Student Attitudes, Intervention
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 2016
How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint…
Descriptors: Sequential Learning, Data Collection, Information Retrieval, Evaluation Methods
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Psychology, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
Kowalski, John; Zhang, Yanhui; Gordon, Geoffrey J. – Journal of Educational Data Mining, 2014
The Pinyin Tutor has been used the past few years at over thirty institutions around the world to teach students to transcribe spoken Chinese phrases into Pinyin. Large amounts of data have been collected from this program on the types of errors students make on this task. We analyze these data to discover what makes this task difficult and use…
Descriptors: Intelligent Tutoring Systems, Chinese, Verbal Communication, Statistical Analysis
Dowell, Nia M. M.; Graesser, Arthur C. – Journal of Learning Analytics, 2014
An emerging trend toward computer-mediated collaborative learning environments promotes lively exchanges between learners in order to facilitate learning. Discourse can play an important role in enhancing epistemology, pedagogy, and assessments in these environments. In this paper, we highlight some of our recent work showing the advantages using…
Descriptors: Cognitive Processes, Affective Behavior, Computational Linguistics, Intelligent Tutoring Systems
Roll, Ido; Baker, Ryan S. J. d.; Aleven, Vincent; Koedinger, Kenneth R. – Journal of the Learning Sciences, 2014
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…
Descriptors: Help Seeking, Comparative Analysis, Behavior Patterns, Intelligent Tutoring Systems
Rau, Martina A.; Aleven, Vincent; Rummel, Nikol – Learning and Instruction, 2013
Research shows that multiple representations can enhance student learning. Many curricula use multiple representations across multiple task types. The temporal sequence of representations and task types is likely to impact student learning. Research on contextual interference shows that interleaving learning tasks leads to better learning results…
Descriptors: Visual Aids, Intelligent Tutoring Systems, Grade 5, Grade 6
Chang, Kai-min; Nelson, Jessica; Pant, Udip; Mostow, Jack – International Journal of Artificial Intelligence in Education, 2013
A new type of sensor for students' mental states is a single-channel portable EEG headset simple enough to use in schools. To gauge its potential, we recorded its signal from children and adults reading text and isolated words, both aloud and silently. We used this data to train and test classifiers to detect (a) when reading is difficult, (b)…
Descriptors: Diagnostic Tests, Reading Comprehension, Reading Difficulties, Difficulty Level

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