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Shi, Genghu; Wang, Lijia; Zhang, Liang; Shubeck, Keith; Peng, Shun; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2021
Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation…
Descriptors: Intelligent Tutoring Systems, Adult Literacy, Adult Students, Reading Comprehension
Chen, Su; Fang, Ying; Shi, Genghu; Sabatini, John; Greenberg, Daphne; Frijters, Jan; Graesser, Arthur C. – Grantee Submission, 2021
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…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Attention, Adult Literacy
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Fang, Ying; Shubeck, Keith; Lippert, Anne; Chen, Qinyu; Shi, Genghu; Feng, Shi; Gatewood, Jessica; Chen, Su; Cai, Zhiqiang; Pavlik, Philip; Frijters, Jan; Greenberg, Daphne; Graesser, Arthur – Grantee Submission, 2018
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Adult Literacy
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Fang, Ying; Shubeck, Keith; Lippert, Anne; Cheng, Qinyu; Shi, Genghu; Feng, Shi; Gatewood, Jessica; Chen, Su; Cai, Zhiqiang; Pavlik, Philip; Frijters, Jan; Greenberg, Daphne; Graesser, Arthur – International Educational Data Mining Society, 2018
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Adult Literacy