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Sun, Jerry Chih-Yuan; Yu, Shih-Jou; Chao, Chih-Hsuan – Educational Psychology, 2019
The current study developed an intelligent learning environment for online education of research ethics and investigated how encouragement and warning intelligent feedback influenced learners' engagement (behavioural, emotional, and cognitive) and cognitive load (mental load and mental effort). Participants included 191 graduate students in Taiwan…
Descriptors: Feedback (Response), Learner Engagement, Cognitive Processes, Difficulty Level
Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – International Journal of Computer-Supported Collaborative Learning, 2019
Research on Computer-Supported Collaborative Learning (CSCL) has provided significant insights into why collaborative learning is effective and how we can effectively provide support for it. Building on this knowledge, we can investigate when collaboration is beneficial to support learning. Specifically, collaborative and individual learning are…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Educational Technology, Intelligent Tutoring Systems
Elamin, Obbey; Rizk, Reham; Adams, John – Education Economics, 2019
We study the effect of private tutoring (PT) on parents' decision to work more using a sample from the Egypt Labor Market Panel Survey in 2012 and apply a semi-parametric recursive bivariate probit model to control for endogeneity. Our finding shows that PT increases father propensity to work overtime by about 2 percentage points (pp) and a…
Descriptors: Tutoring, Urban Areas, Parent Attitudes, Decision Making
Song, Donggil; Rice, Marilyn; Oh, Eun Young – International Review of Research in Open and Distributed Learning, 2019
Online learning environments could be well understood as a multifaceted phenomenon affected by different aspects of learner participation including synchronous/asynchronous interactions. The aim of this study was to investigate learners' participation in online courses, synchronous interaction with a conversational virtual agent, their…
Descriptors: Online Courses, Educational Technology, Technology Uses in Education, Interaction
Rozo, Hugo; Real, Miguel – Journal of Technology and Science Education, 2019
The present article constitutes a systematic review of the literature with the objective of identifying the appropriate elements that must be considered when designing and creating adaptive digital educational resources. The methodological process was rigorous and systematic, employing an article search in which the texts related to the object of…
Descriptors: Instructional Design, Intelligent Tutoring Systems, Instructional Materials, Educational Technology
Hamouda, Sally; Edwards, Stephen H.; Elmongui, Hicham G.; Ernst, Jeremy V.; Shaffer, Clifford A. – ACM Transactions on Computing Education, 2019
Recursion is one of the most important and hardest topics in lower division computer science courses. As it is an advanced programming skill, the best way to learn it is through targeted practice exercises. But the best practice problems are time consuming to manually grade by an instructor. As a consequence, students historically have completed…
Descriptors: Computer Science Education, Programming, Instructional Effectiveness, Difficulty Level
Harmon, Jon; Warnakulasooriya, Rasil – International Educational Data Mining Society, 2019
The Additive Factor Model (AFM) is a cognitive diagnostic model that can be used to predict student performance on items in a context that allows for student learning. Within AFM, "skills" have a learning rate, and student acquisition of a skill depends only on the number of opportunities a student has had to exercise that skill and the…
Descriptors: Electronic Learning, Factor Analysis, Goodness of Fit, Item Response Theory
Dang, Steven; Koedinger, Ken – International Educational Data Mining Society, 2019
A student's ability to regulate their thoughts, emotions and behaviors in the face of temptation is linked to their task specific motivational goals and dispositions. Behavioral tasks are designed to strain a targeted resource to differentiate individuals through measures of their performance. In this paper, we explore how student behavior on…
Descriptors: Correlation, Self Management, Student Motivation, Student Behavior
Nguyen, Huy; Wang, Yeyu; Stamper, John; McLaren, Bruce M. – International Educational Data Mining Society, 2019
Knowledge components (KCs) define the underlying skill model of intelligent educational software, and they are critical to understanding and improving the efficacy of learning technology. In this research, we show how learning curve analysis is used to fit a KC model--one that was created after use of the learning technology--which can then be…
Descriptors: Middle School Students, Knowledge Representation, Models, Computer Games
McCarthy, Kathryn S.; Roscoe, Rod D.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2019
This study investigated the effect of incorporating spelling and grammar checking tools within an automated writing tutoring system, Writing Pal. High school students (n = 119) wrote and revised six persuasive essays. After initial drafts, all students received formative feedback about writing strategies. Half of the participants were also given…
Descriptors: Spelling, Grammar, Automation, Writing Instruction
Lippert, Anne; Gatewood, Jessica; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2019
One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Adult Literacy, Skill Development
Saastamoinen, Kalle; Rissanen, Antti – International Baltic Symposium on Science and Technology Education, 2019
Conventional learning guidance systems are typically automated machines for creating teaching materials: quizzes, exercises, examinations etc. In the future, systems will also offer ease of use, attention to sociality, ability to adapt to the pupil's needs and skill levels, and time savings. Ease-of-use and adaptation can be sought using systems…
Descriptors: Teaching Methods, Intelligent Tutoring Systems, Artificial Intelligence, Usability
Sam Allen Patton III – ProQuest LLC, 2019
The capacity to read nonfiction text with understanding is of obvious and vital importance, yet many students struggle to do so. This randomized control trial extends previous research by contrasting the efficacy of a standard, or Comp, tutoring program with a second Comp tutoring program that included strategies for transferring learning to new…
Descriptors: At Risk Students, Reading Comprehension, Direct Instruction, Nonfiction
Smith, E. Halle; Hollander, John; Graesser, Art C.; Sabatini, John; Hu, Xiangen – English Teaching, 2021
Facing the demands of the pandemic and distance learning, English learners require educational technologies that are accessible, engaging, and effective. Meeting these demands requires educational technology developers to consider learners' sociocultural contexts. Learning theories can be applied to meet individuals' needs to optimize chances for…
Descriptors: Reading Comprehension, English (Second Language), Second Language Learning, Second Language Instruction
Nkiko, Mojisola O. – Journal of Education and Learning, 2021
The indispensability and vast career possibilities associated with Chemistry notwithstanding, there is a palpable growing decline enrollment in Chemistry in Nigerian universities, particularly the private universities. The paper interrogated the teaching and learning of Chemistry in Nigerian private universities with a view to re-awakening the…
Descriptors: Foreign Countries, Chemistry, Science Instruction, College Science

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