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

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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Development of an Interactive Narrative for Adult Literacy Learners

End-User Considerations in Educational Technology Design - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-2639-1.ch012 ◽

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.

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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

Journal of Educational Computing Research ◽

10.1177/0735633117696909 ◽

2017 ◽

Vol 55 (7) ◽

pp. 1022-1048 ◽

Cited By ~ 1

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.

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Using latent transition analysis to identify effects of an intelligent tutoring system on reading comprehension of seventh-grade students

Reading and Writing ◽

10.1007/s11145-018-9888-8 ◽

2018 ◽

Vol 31 (9) ◽

pp. 2095-2113

Author(s):

Xuejun Ryan Ji ◽

Andrea Beerwinkle ◽

Kausalai Wijekumar ◽

Puiwa Lei ◽

R. Malatesha Joshi ◽

...

Keyword(s):

Reading Comprehension ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Latent Transition Analysis ◽

Seventh Grade ◽

Tutoring System ◽

Transition Analysis ◽

Latent Transition

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The Impact of Learning Software on Improving Reading Comprehension Skills

International Journal of e-Education e-Business e-Management and e-Learning ◽

10.17706/ijeeee.2020.10.3.235-248 ◽

2020 ◽

Vol 10 (3) ◽

pp. 235-248

Author(s):

Rina Azoulay ◽

◽

Esther David ◽

Mireille Avigal ◽

Dorit Hutzler

Keyword(s):

Reading Comprehension ◽

High School Students ◽

Intelligent Tutoring System ◽

Difficulty Level ◽

School Students ◽

Learning Gain ◽

Academic Level ◽

Comprehension Skills ◽

The Impact ◽

Reading Comprehension Skills

One of the challenges of an intelligent tutoring system (ITS) is adapting the difficulty level of the questions posed to the student to suit the student’s academic level. Our study examines the task of adjusting the system’s level of challenges to the level of the learner and addresses the questions of how best to do so and whether there is any benefit from such adjustment. To answer these questions, we developed reading comprehension courseware that includes three adaptive algorithms for adjusting the level of the questions presented to the students: the random selection algorithm, the Q-learning based algorithm, and the Bayesian inference algorithm. We conduct a real-world experiment in which real high school students used the courseware to improve their reading comprehension skills. In order to compare and evaluate the performance of the algorithms, the courseware used by each student utilized one of the three adaptive algorithm alternatives. Our results demonstrate that when considering all of the students, there was significant improvement (learning gain) using each of the methods.

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