Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum

Najar, Amir Shareghi; Mitrović, Antonija; McLaren, Bruce M.

doi:10.1007/s11257-016-9181-y

Cited by 28 publications

(9 citation statements)

References 46 publications

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“…Based on our results, learners who self-explained performed significantly better on conceptual questions than those who did not. Our results are supported by literature showing SE enhances conceptual knowledge (Chi et al 1989;Chi et al, 1994;Ferguson-Hessler and de Jong, 1990;Najar, Mitrovic, and McLaren, 2016). Furthermore, we have provided evidence that this also holds for learning with a smartphone tutor.…”

Section: Discussionsupporting

confidence: 88%

“…Selfexplanation was first introduced as open-ended questions requiring the learner to explain presented learning material to oneself, to gain deeper understanding (Chi et al 1989). Previous research provides evidence that selfexplaining enhances conceptual knowledge (Najar, Mitrovic and McLaren, 2016). Several types of SE prompts are reported in literature (Wylie and Chi, 2014), including menu-based SE prompts.…”

Section: Introductionmentioning

confidence: 99%

“…Prior work on SE used either worked examples, e.g. (Berthold, Eysink and Renkl 2009;Najar, Mitrovic and McLaren, 2016), or problem solving (unsupported or tutored), e.g. (Kwon, Kumalasari and Howland 2011).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor

Fabic

Mitrović

Neshatian

2019

Int J Artif Intell Educ

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The overarching goal of our project is to design effective learning activities for PyKinetic, a smartphone Python tutor. In this paper, we present a study using a variant of Parsons problems we designed for PyKinetic. Parsons problems contain randomized code which needs to be reordered to produce the desired effect. In our variant of Parsons problems, students were asked to complete the missing part(s) of some lines of code (LOCs), and rearrange the LOCs to match the problem description. In addition, we added menu-based Self-Explanation (SE) prompts. Students were asked to self-explain concepts related to incomplete LOCs they solved. Our hypotheses were: (H1) PyKinetic would be successful in supporting learning; (H2) menu-based SE prompts would result in further learning benefits; (H3) students with low prior knowledge (LP) would learn more from our Parsons problems in comparison to those with high prior knowledge (HP). We found that the participants' scores on the post-test improved, thus showing evidence of learning in PyKinetic. The experimental group participants, who had SE prompts, showed improved learning in comparison to the control group. Further analyses revealed that LP students improved more than HP students and the improvement is even more pronounced for LP learners who selfexplained. The contributions of our work are a) pedagogically-guided design of Parsons problems with SE prompts used on smartphones, b) showing that our Parsons problems are effective in supporting learning and c) our Parsons problems with SE prompts are especially effective for students with low prior knowledge.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor

Fabic

Mitrović

Neshatian

2019

Int J Artif Intell Educ

Self Cite

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“…In a faded example, one or two steps are omitted and the learner is expected to complete them. Learners using the adaptive sequence learned both more and faster than the group using the fixed sequence (Najar, Mitrovic, & McLaren, ).…”

Section: Screen‐level Pedagogy: Example Systemsmentioning

confidence: 99%

Escape from the Skinner Box: The case for contemporary intelligent learning environments

Boulay¹

2019

Brit J Educational Tech

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Intelligent Tutoring systems (ITSs) and Intelligent Learning Environments (ILEs) have been developed and evaluated over the last 40 years. Recent meta‐analyses show that they perform well enough to act as effective classroom assistants under the guidance of a human teacher. Despite this success, they have been criticised as embodying a retrograde behaviourist technology. They have also been caught up in broader controversies about the role of Artificial Intelligence in society and about the entry of big data companies into the education market and the harvesting of learner data. This paper concentrates on rebutting the criticisms of the pedagogy of ITSs and ILEs. It offers examples of how a much wider range of pedagogies are available than their critics claim. These wider pedagogies operate at both the screen level of individual systems, as well as at the classroom level within which the systems are orchestrated by the teacher. It argues that there are many ways that such systems can be integrated by the teacher into the overall experience of a class. Taken together, the screen‐level and orchestration‐level dramatically enlarge the range of pedagogies beyond what was possible with the “Skinner Box.”

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“…Then, these systems create similar exercises, usually changing data, which can be solved by following the learnt steps. In subsequent stages, they may also gradually fade out the number of steps there are in the worked solution (faded worked examples) so that the students can explain how they arrived to the final solution [11] , [12] , [13] , [14] .…”

Section: Introductionmentioning

confidence: 99%

An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

Castro-Schez

Glez-Morcillo

Albusac

et al. 2021

Information Sciences

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Highlights We design and develop an intelligent tutoring system to encourage students to learn through experimentation We allow to work at higher levels of Bloom’s Taxonomy with the designed intelligent tutoring system We use artificial intelligence and fuzzy rules to design and develop the learning instructor We establish foundations to build intelligent tutoring system that allow to learn through experimentation

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Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum

Cited by 28 publications

References 46 publications

Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor

Evaluation of Parsons Problems with Menu-Based Self-Explanation Prompts in a Mobile Python Tutor

Escape from the Skinner Box: The case for contemporary intelligent learning environments

An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

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