Intelligent tutoring systems and learning outcomes: A meta-analysis

By Wenting Ma, Olusola O. Adesope, John C. Nesbit, Qing Liu. 2014

705 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/9581009c34216cac062888c5ccf055453db17881

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. John Dexter Fletcher
2. Gwo-Jen Hwang
3. Mason R. Lefler
4. Jia Zhou
5. Benjamin Wan-Sang Wah
6. Pei-Luen Patrick Rau
7. Brian R. Belland
8. Andrew E. Walker

In Computer Science Applications

1. Haoran Xie
2. Dragan Gasevic
3. John Dexter Fletcher
4. Gwo-Jen Hwang
5. Mason R. Lefler
6. Brian R. Belland
7. Andrew E. Walker
8. J. Kulik

In Developmental and Educational Psychology

1. Haoran Xie
2. John Dexter Fletcher
3. Gwo-Jen Hwang
4. Mason R. Lefler
5. Pei-Luen Patrick Rau
6. Masaaki Kurosu
7. Gavriel Salvendy
8. James R. Lewis

Citations in the corpus

• Vision, challenges, roles and research issues of Artificial Intelligence in Education (2020, background): “Several meta-analytic studies have demonstrated the effectiveness of intelligent tutoring systems to promote learning outcomes (Ma, Adesope, Nesbit, & Liu, 2014; Steenbergen-Hu & Cooper, 2014; VanLehn, 2011).”
• Inducing Self-Explanation: a Meta-Analysis (2018, neutral): “Computer-based instruction (CBI) was defined as offering response feedback with no student modeling, and intelligent tutoring systems (ITS) were defined as modeling student knowledge to adapt feedback, task selection, or prompts (Ma et al. 2014).”
• Seven HCI Grand Challenges (2019, positive): “ITSs provide individualized instruction and have the potential for significantly higher achievement outcomes than other modes of instruction (except small-group human tutoring and individual human tutoring when results were comparable) (Ma, Adesope, Nesbit, & Liu, 2014).”
• Effectiveness of Intelligent Tutoring Systems (2016, methodology): “Finally, the meta-analysis by Ma et al. (2014) analyzed 107 findings from 73 separate reports.”
• The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis (2020, background): “The assumption of a novelty effect is not in line with the result of Ma et al. (2014), who found that interventions with durations of less than four weeks denote less-positive student learning outcomes.”
• Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education (2016, methodology): “63 in humanities and social science (Ma et al., 2014).”