scholarly journals Development of an Intelligent Tutoring System for English Reading Comprehension: Design Based on Philippine Public School Flexible Learning Experience

2021 ◽  
Vol 13 (5) ◽  
pp. 9-23
Author(s):  
Day Bert R. Mariñas ◽  
◽  
Roberto R. Coloma ◽  
Lin V. Tadeja ◽  
Shaira Marie J. Castillo ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Public School ◽  
Intelligent Tutoring ◽  
Learning Experience ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Flexible Learning ◽  
English Reading ◽  
English Reading Comprehension

Development of an Interactive Narrative for Adult Literacy Learners

2018 ◽  
pp. 238-262
Author(s):  
Amy M. Johnson ◽  
Elizabeth L. Tighe ◽  
Matthew E. Jacovina ◽  
G. Tanner Jackson ◽  
Danielle S. McNamara
Keyword(s):  
Reading Comprehension ◽  
Adult Learners ◽  
Adult Literacy ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Strategy Instruction ◽  
Interactive Narrative ◽  
Tutoring System ◽  
Active Reading ◽  
Literacy Learners

This chapter describes development efforts that build upon the Interactive Strategy Trainer for Active Reading and Thinking-2 (iSTART-2), an intelligent tutoring system that provides self-explanation strategy instruction to improve reading comprehension. The chapter reflects on considerations of the unique needs of adult literacy learners, and outlines the specific guidelines followed to adapt the system to these learners. Several modifications have been made to adapt iSTART to adult learners, including the following: 1) two additional strategy instructional modules for summarization and deep question asking, 2) a text library with life-relevant texts for adult learners, and 3) an interactive narrative which allows instantiated practice of reading strategies using life-relevant artifacts. The authors also describe results from two attitudinal studies examining learners' perceptions of the interactive narrative.

Evidence of an Intelligent Tutoring System as a Mindtool to Promote Strategic Memory of Expository Texts and Comprehension With Children in Grades 4 and 5

2017 ◽  
Vol 55 (7) ◽  
pp. 1022-1048 ◽  
Author(s):  
Kausalai (Kay) Wijekumar ◽  
Bonnie J. F. Meyer ◽  
Puiwa Lei ◽  
Weiyi Cheng ◽  
Xuejun Ji ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Prior Knowledge ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Text Structure ◽  
Tutoring System ◽  
Randomized Controlled Studies ◽  
Strategic Memory ◽  
Memory Structures

Reading and comprehending content area texts require learners to effectively select and encode with hierarchically strategic memory structures in order to combine new information with prior knowledge. Unfortunately, evidence from state and national tests shows that children fail to successfully navigate the reading comprehension challenges they face. Schools have struggled to find approaches that can help children succeed in this important task. Typical instruction in classrooms across the country has focused on procedural application of strategies or content-focused approaches that encourage rich discussions. Both approaches have achieved success but have limitations-related transparency and specificity of scaffolds and guidance for the teacher and learner in today’s diverse and complex classroom settings. The text structure strategy combines content and strategy to provide pragmatic, transparent, and scaffolded instruction addressing these challenges. A web-based intelligent tutoring system for the text structure strategy, named ITSS, was designed and developed to provide consistent and high-quality instruction to learners in Grades 4 and 5 about how to read, select main ideas, encode strategic memory structures, make inferences, and monitor comprehension during reading. In this article, we synthesize results from two recent large-scale randomized controlled studies to showcase how the ITSS supports selection and encoding of students’ strategic memory structures and how prior knowledge affects the memory structures. We provide greater depth of information about such processing than examined and reported in extant literature about overall increases in reading comprehension resulting from students using ITSS.

scholarly journals Exploring an intelligent tutoring system as a conversation-based assessment tool for reading comprehension

2018 ◽  
Vol 45 (2) ◽  
pp. 615-633 ◽  
Author(s):  
Genghu Shi ◽  
Anne M. Lippert ◽  
Keith Shubeck ◽  
Ying Fang ◽  
Su Chen ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Assessment Tool ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System

scholarly journals An Adaptive and Interactive Agent Based ITS for Cognitive Skill Prediction and Improvement

2018 ◽  
Vol 4 (2) ◽  
pp. 604-612
Author(s):  
Mrs. R. Gowri ◽  
Dr. S. Kanmani ◽  
M. Santhosh ◽  
S. Naresh
Keyword(s):  
Cognitive Ability ◽  
Help Seeking ◽  
Intelligent Tutoring ◽  
Learning Experience ◽  
Intelligent Tutoring System ◽  
Cognitive Skill ◽  
Game Based Learning ◽  
Tutoring System ◽  
Agent Based ◽  
Course Materials

This paper proposes an adaptive and interactive agent based intelligent tutoring system for cognitive ability realization and improvement (ITSCARE - Intelligent Tutoring System for Cognitive Ability Realization and Improvement). ITSCARE allows the learners to realize their cognitive ability and to improve their cognition while studying the course. It provides different types of course materials which are dynamically adapted. It also increases the confidence level of the learners and provides an effective learning experience. This system also provides game based learning which influences the learners to get motivated and focus on the course. After the completion of each chapter, a test is conducted to predict the cognitive ability where students are assessed using their help seeking skills during the test and cognitive skill factors such as memory, concentration, attention in detail etc,. It uses politeness style to provide the test results and feed back to the students which keep the learner interest in the subject. Collaborative learning among the learner is improved by conducting quiz competition where a group of students participate and a winner is chosen. It uses different type of software agent to predict and improve the cognitive skill.

Development of an Interactive Narrative for Adult Literacy Learners

2021 ◽  
pp. 520-544
Author(s):  
Amy M. Johnson ◽  
Elizabeth L. Tighe ◽  
Matthew E. Jacovina ◽  
G. Tanner Jackson ◽  
Danielle S. McNamara
Keyword(s):  
Reading Comprehension ◽  
Adult Learners ◽  
Adult Literacy ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Strategy Instruction ◽  
Interactive Narrative ◽  
Tutoring System ◽  
Active Reading ◽  
Literacy Learners

This chapter describes development efforts that build upon the Interactive Strategy Trainer for Active Reading and Thinking-2 (iSTART-2), an intelligent tutoring system that provides self-explanation strategy instruction to improve reading comprehension. The chapter reflects on considerations of the unique needs of adult literacy learners, and outlines the specific guidelines followed to adapt the system to these learners. Several modifications have been made to adapt iSTART to adult learners, including the following: 1) two additional strategy instructional modules for summarization and deep question asking, 2) a text library with life-relevant texts for adult learners, and 3) an interactive narrative which allows instantiated practice of reading strategies using life-relevant artifacts. The authors also describe results from two attitudinal studies examining learners' perceptions of the interactive narrative.

scholarly journals Automated Disengagement Tracking Within an Intelligent Tutoring System

2021 ◽  
Vol 3 ◽  
Author(s):  
Su Chen ◽  
Ying Fang ◽  
Genghu Shi ◽  
John Sabatini ◽  
Daphne Greenberg ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Sentence Processing ◽  
Adult Literacy ◽  
Digital Literacy ◽  
Text Comprehension ◽  
Intelligent Tutoring ◽  
Tracking System ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Literacy Learners

This paper describes a new automated disengagement tracking system (DTS) that detects learners’ maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items.

scholarly journals Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System

2021 ◽  
Author(s):  
Ying Fang ◽  
Anne Lippert ◽  
Zhiqiang Cai ◽  
Su Chen ◽  
Jan C. Frijters ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Response Time ◽  
Struggling Readers ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Psychometric Tests ◽  
Tutoring System ◽  
Lower Accuracy ◽  
Comprehension Skills ◽  
Reading Comprehension Skills

AbstractA common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an intelligent tutoring system with computer agents (AutoTutor) designed to improve comprehension skills in adults with low reading literacy. One goal of this study was to classify adults into different clusters based on their behavioral patterns (accuracy and response time to answer questions) while they interacted with AutoTutor to help them improve their reading comprehension skills. A second goal was to investigate whether adults’ behaviors were associated with different reading components. A third goal was to assess improvements in reading comprehension skills, based on psychometric tests, of different clusters of readers. Performance on AutoTutor was collected in a targeted 100-hour hybrid intervention for adult readers (n = 252) that included both human teachers and the AutoTutor system. The adults’ average accuracy and response time in AutoTutor were used to cluster the adults into four categories: higher performers (comparatively fast and accurate), conscientious readers (slow but accurate), under-engaged readers (fast at the expense of somewhat lower accuracy) and struggling readers (slow and inaccurate). Two psychometric tests of comprehension were used to assess comprehension. Gains in comprehension scores were highest for conscientious readers, lowest for struggling readers, with higher performing readers and under-engaged readers in between. The results provide guidance to enhance the adaptivity of AutoTutor.

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