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an de Sande, Brett – International Educational Data Mining Society, 2016
Learning curves have proven to be a useful tool for understanding how a student learns a given skill as they progress through a curriculum. A learning curve for a given Knowledge Component (KC) is a plot of some measure of competence as a function of the number of opportunities the student has had to apply that KC. Consider the case where each…
Descriptors: Learning Processes, Knowledge Level, Problem Solving, Homework
Aguilar, Jose; Cordero, Jorge; Buendía, Omar – Journal of Educational Computing Research, 2018
In this article, we propose the concept of "Autonomic Cycle Of Learning Analysis Tasks" (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic…
Descriptors: Data Analysis, Learning Processes, Decision Making, Instructional Improvement
Bernacki, Matthew L.; Walkington, Candace – Journal of Educational Psychology, 2018
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…
Descriptors: High School Students, Student Interests, Individualized Instruction, Mathematics Instruction
Dounas, Lamiae; Salinesi, Camille; Beqqali, Omar El – Journal of Information Technology Education: Research, 2019
Aim/Purpose: In this paper, we highlight the need to monitor and diagnose adaptive e-learning systems requirements at runtime to develop a better understanding of their behavior during learning activities and improve their design. Our focus is to reveal which learning requirements the adaptive system is satisfying while still evolving and to…
Descriptors: Electronic Learning, Learning Activities, Instructional Design, Accuracy
Shukla, Saurabh; Shivakumar, Ashutosh; Vasoya, Miteshkumar; Pei, Yong; Lyon, Anna F. – International Association for Development of the Information Society, 2019
In this research paper, we present an AR- and AI-based mobile learning tool that provides: 1.) automatic and accurate intelligibility analysis at various levels: letter, word, phrase and sentences, 2.) immediate feedback and multimodal coaching on how to correct pronunciation, and 3.) evidence-based dynamic training curriculum tailored to each…
Descriptors: Bilingualism, Special Education, Pronunciation Instruction, Feedback (Response)
Fang, Ying; Nye, Benjamin; Pavlik, Philip; Xu, Yonghong Jade; Graesser, Arthur; Hu, Xiangen – International Educational Data Mining Society, 2017
Student persistence in online learning environments has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.). The current examines student persistence in an adaptive learning environment, ALEKS (Assessment and LEarning in Knowledge Spaces). Specifically, the study explores the…
Descriptors: Learning Processes, Academic Persistence, Correlation, Academic Achievement
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – International Educational Data Mining Society, 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: Intelligent Tutoring Systems, Sequential Approach, Problem Solving, Learning Processes
Miller, Chyna J.; Bernacki, Matthew L. – High Ability Studies, 2019
The ability to self-regulate learning (SRL) is a skill theorized to transfer across learning environments. Students with this ability can consider a learning task, identify a goal, develop a plan to achieve it, execute that plan, and monitor and adapt learning until the goal is met. This paper examines the educational implications of developing…
Descriptors: Case Studies, Mathematics Achievement, Metacognition, Learning Strategies
Brenner, Daniel G.; Matlen, Bryan J.; Timms, Michael J.; Gochyyev, Perman; Grillo-Hill, Andrew; Luttgen, Kim; Varfolomeeva, Marina – Technology, Knowledge and Learning, 2017
This study investigated how the frequency and level of assistance provided to students interacted with prior knowledge to affect learning in the "Voyage to Galapagos" ("VTG") science inquiry-learning environment. "VTG" provides students with the opportunity to do simulated science field work in Galapagos as they…
Descriptors: Learning Processes, Prior Learning, Online Courses, Science Education
Lallé, Sébastien; Conati, Cristina; Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle – International Educational Data Mining Society, 2017
In this paper, we investigate the relationship between students' learning gains and their compliance with prompts fostering self-regulated learning (SRL) during interaction with MetaTutor, a hypermedia-based intelligent tutoring systems (ITS). When possible, we evaluate compliance from student explicit answers on whether they want to follow the…
Descriptors: Compliance (Psychology), Metacognition, Computer Software, Eye Movements
Streeter, Matthew – International Educational Data Mining Society, 2015
We show that student learning can be accurately modeled using a mixture of learning curves, each of which specifies error probability as a function of time. This approach generalizes Knowledge Tracing [7], which can be viewed as a mixture model in which the learning curves are step functions. We show that this generality yields order-of-magnitude…
Descriptors: Probability, Error Patterns, Learning Processes, Models
Mastorodimos, Dimitrios; Chatzichristofis, Savvas A. – Journal of Educational Technology Systems, 2019
Students face difficulties in learning mathematical processes. As a result, they have negative emotions toward mathematics. The use of technology is employed to change the student's attitude toward mathematics. Some methods utilize intelligent tutoring systems to recognize student's emotional state and adapt the learning process accordingly. These…
Descriptors: Mathematics Instruction, Mathematical Concepts, Intelligent Tutoring Systems, Learning Processes
Malekzadeh, Mehdi; Mustafa, Mumtaz Begum; Lahsasna, Adel – Educational Technology & Society, 2015
Having improved emotional (affective) state may have several benefits on learners, such as promoting higher cognitive flexibility and opens the learner to discovery of new ideas and possibilities. On other side, negative emotional states like boredom and frustration have been linked with less use of self-regulation and cognitive strategies for…
Descriptors: Intelligent Tutoring Systems, Emotional Response, Self Control, Cognitive Processes
Heift, Trude; Schulze, Mathias – Language Teaching, 2015
"Sometimes maligned for its allegedly behaviorist connotations but critical for success in many fields from music to sport to mathematics and language learning, 'practice' is undergoing something of a revival in the applied linguistics literature" (Long & Richards 2007, p. xi). This research timeline provides a systematic overview of…
Descriptors: Computer Assisted Instruction, Second Language Instruction, Second Language Learning, Learning Processes
Doolittle, Peter E.; Bryant, Lauren H.; Chittum, Jessica R. – British Journal of Educational Technology, 2015
The construction of asynchronous learning environments often involves the creation of self-paced multimedia instructional episodes that provide the learner with control over the pacing of instruction (segmentation); however, does the amount of segmentation impact learning? This study explored the effects of the degree of segmentation on recall and…
Descriptors: Multimedia Instruction, Asynchronous Communication, Pacing, Learner Controlled Instruction

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