Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning

By Beverly Park Woolf. 2008

815 citations recorded by the source corpus.

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…and Benbasat, 2003; Polson and Richardson, 1988; Psotka, Massey, and Mutter, 1988; Ritter and Feurzeig, 1988; Silverman, 1992; Sleeman and Brown, 1982; Southwick, 1991; Swartout, Paris, and Moore, 1991; Tanner and Keuneke, 1991; Tintarev, 2007; Woolf, 2007; Woolf, Beck, Eliot, and Stern, 2001)”
The future of employment: How susceptible are jobs to computerisation? (2017, background): “ibrated to match individual student needs (Woolf, 2010).”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, background): “A gentle introduction to the topic can be found in Jameson (1995) and a more recent surveys can be found in Mayo and Mitrovic (2001), Almond et al. (2007) and Reye (2004), and in recent books by Woolf (2009) and Nkambou et al. (2010).”
Empowering educators to be AI-ready (2022, background): “Readers are referred to (Luckin, 2016, Mavrikis, Cukurova, Di Mitri, Schneider, & Draschler, 2021; Roll & Wylie, 2016; Woolf, 2010) for more detailed reviews of a historical view of the field.”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, background): “2 Early AutoTutor Interface (Graesser et al. 2008, 2014; Halpern et al. 2012; McNamara and Magliano 2009; Millis et al. 2011; Woolf 2009).”
Affect-aware tutors: recognising and responding to student affect (2009, background): “These techniques are very versatile and have been used with intelligent tutors to answer a variety of questions (Woolf, 2009): Is the student engaged? Is the student motivated? What type of intervention should be tried next? How is learning progress measured? How can student success be recognised? H”
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020, methodology): “g is one of the machine learning techniques that has been used with intelligent tutors to partially automate construction of the student model and to automatically compute an optimal teaching policy (Woolf, 2007). One advantage of using RL methods in tutoring is that the model can learn adaptive tea”
A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning (2014, unknown): “Many ITS are designed to follow the practices of human tutors (Graesser et al., 2011; Woolf, 2009).”
Context-Aware Attentive Knowledge Tracing (2020, background): “Recent advances in data analytics and intelligent tutoring systems [32] have enabled the collection and analysis of large-scale learner data; these advances hint at the potential of personalized learning at large scale, by automatically providing personalized feedback [24] and learning activity reco”