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Cultivating Undergraduates' Plagiarism Avoidance Knowledge and Skills with an Online Tutorial System
Liu, Gi-Zen; Lu, Hui-Ching; Lin, Vivien; Hsu, Wei-Chen – Journal of Computer Assisted Learning, 2018
With the increased use of digital materials, undergraduate writers in English as a foreign language (EFL) contexts have become more susceptible to plagiarism. In this study, the researchers designed a blended English writing course with an online writing tutorial system entitled "DWright." The study examined the effectiveness of the…
Descriptors: Undergraduate Students, Plagiarism, Prevention, Intelligent Tutoring Systems
Weragama, Dinesha; Reye, Jim – International Journal of Artificial Intelligence in Education, 2014
Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Programming Languages
Steenbergen-Hu, Saiying; Cooper, Harris – Journal of Educational Psychology, 2014
This meta-analysis synthesizes research on the effectiveness of intelligent tutoring systems (ITS) for college students. Thirty-five reports were found containing 39 studies assessing the effectiveness of 22 types of ITS in higher education settings. Most frequently studied were AutoTutor, Assessment and Learning in Knowledge Spaces, eXtended…
Descriptors: Meta Analysis, Intelligent Tutoring Systems, Instructional Effectiveness, College Students
Nakamura, Christopher M.; Murphy, Sytil K.; Christel, Michael G.; Stevens, Scott M.; Zollman, Dean A. – Physical Review Physics Education Research, 2016
Computer-automated assessment of students' text responses to short-answer questions represents an important enabling technology for online learning environments. We have investigated the use of machine learning to train computer models capable of automatically classifying short-answer responses and assessed the results. Our investigations are part…
Descriptors: Physics, Introductory Courses, Science Instruction, Intelligent Tutoring Systems
Rummel, Nikol; Walker, Erin; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2016
In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that--due to better-designed technology, grounded in research--avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years…
Descriptors: Artificial Intelligence, Cooperative Learning, Futures (of Society), Electronic Learning
Chou, Chih-Yueh; Chan, Tak-Wai – International Journal of Artificial Intelligence in Education, 2016
"Reciprocal tutoring," as reported in "Exploring the design of computer supports for reciprocal tutoring" (Chan and Chou 1997), has extended the meaning and scope of "intelligent tutoring" originally implemented in stand alone computers. This research is a follow-up to our studies on a "learning companion…
Descriptors: Peer Teaching, Tutoring, Cognitive Processes, Difficulty Level
Hoppe, H. Ulrich – International Journal of Artificial Intelligence in Education, 2016
The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…
Descriptors: Guidelines, Intelligent Tutoring Systems, Cooperative Learning, Modeling (Psychology)
VanLehn, Kurt; Chung, Greg; Grover, Sachin; Madni, Ayesha; Wetzel, Jon – International Journal of Artificial Intelligence in Education, 2016
A common hypothesis is that students will more deeply understand dynamic systems and other complex phenomena if they construct computational models of them. Attempts to demonstrate the advantages of model construction have been stymied by the long time required for students to acquire skill in model construction. In order to make model…
Descriptors: Models, Science Instruction, Intelligent Tutoring Systems, Teaching Methods
Suleman, Raja M.; Mizoguchi, Riichiro; Ikeda, Mitsuru – International Journal of Artificial Intelligence in Education, 2016
Negotiation mechanism using conversational agents (chatbots) has been used in Open Learner Models (OLM) to enhance learner model accuracy and provide opportunities for learner reflection. Using chatbots that allow for natural language discussions has shown positive learning gains in students. Traditional OLMs assume a learner to be able to manage…
Descriptors: Metacognition, Intelligent Tutoring Systems, Natural Language Processing, Models
Baer, Whitney O.; Cheng, Qinyu; McGlown, Cadarrius; Gong, Yan; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2016
The Center for the Study of Adult Literacy (CSAL) seeks to improve our understanding of ways to advance the reading skills of adult learners. Our web-based instructional tutor uses trialogues in the AutoTutor framework to deliver lessons in reading comprehension. We have found a way to manipulate proven comprehension strategies to fit the daily…
Descriptors: Adult Learning, Adult Students, Literacy Education, Adult Literacy
Mitrovic, Antonija; Ohlsson, Stellan; Barrow, Devon K. – Computers & Education, 2013
Tutoring technologies for supporting learning from errors via negative feedback are highly developed and have proven their worth in empirical evaluations. However, observations of empirical tutoring dialogs highlight the importance of positive feedback in the practice of expert tutoring. We hypothesize that positive feedback works by reducing…
Descriptors: Tutoring, Feedback (Response), Tutors, Intelligent Tutoring Systems
Jonsdottir, Anna Helga; Bjornsdottir, Audbjorg; Stefansson, Gunnar – Journal of Statistics Education, 2017
A repeated crossover experiment comparing learning among students handing in pen-and-paper homework (PPH) with students handing in web-based homework (WBH) has been conducted. The system used in the experiments, the tutor-web, has been used to deliver homework problems to thousands of students in mathematics and statistics over several years.…
Descriptors: Homework, Web Based Instruction, Conventional Instruction, Comparative Analysis
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

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