“…1 highlights the innovative aspects provided by the presented method regarding existing tools. It should be noted that although the fourth level is empty in this particular field (i.e predictive parsing concept and techniques learning), it is the level where all current research is located, which focuses on improving instructional mechanisms [15] , [16] , [17] , [18] , [19] , [23] , [24] , [25] . Fig.…”
Section: Context and Problem Statementmentioning
confidence: 99%
“…Research within both approaches focuses primarily on adapting the rules which recommend learning actions than on giving the learner the freedom to experiment with [15] , [16] , [17] , [18] , [19] . Experimentation in both approaches is controlled, that is, the instructor establishes the exercises that learners should or can do.…”
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
“…1 highlights the innovative aspects provided by the presented method regarding existing tools. It should be noted that although the fourth level is empty in this particular field (i.e predictive parsing concept and techniques learning), it is the level where all current research is located, which focuses on improving instructional mechanisms [15] , [16] , [17] , [18] , [19] , [23] , [24] , [25] . Fig.…”
Section: Context and Problem Statementmentioning
confidence: 99%
“…Research within both approaches focuses primarily on adapting the rules which recommend learning actions than on giving the learner the freedom to experiment with [15] , [16] , [17] , [18] , [19] . Experimentation in both approaches is controlled, that is, the instructor establishes the exercises that learners should or can do.…”
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
“…A further improvement is adaptive fading, where the level of fading and/or the choice between a regular worked example, a faded example, or a problem is determined based on the students' understanding of the examples [62] or their performance [45]. More recent research explored an adaptive alternation of worked examples, problem-solving, and erroneous examples [13,14], a promising alternative to the faded examples [40].…”
Section: Worked Examples In Problem-solvingmentioning
confidence: 99%
“…The earliest successful research showing the value of examples for learning programming dates back to the early 1980s, when Pirolli and Anderson [54] reported that examples are helpful for guiding students to solutions for novel and difficult problems. Since then, a considerable amount of research has been devoted to the development of example-based learning environments to support students in learning programming in various programming languages, such as LISP [25,36,74,75,76], Prolog [7], C/Java [9,22,39,66], Javascript [19], SQL [13,14,45], and mini-languages [8,78].…”
This research is focused on how to support students' acquisition of program construction skills through worked examples. Although examples have been consistently proven to be valuable for student's learning, the learning technology for computer science education lacks program construction examples with interactive elements that could engage students. The goal of this work is to investigate the value of the "engaging" features in programming examples. We introduce PCEX, an online tool developed to present program construction examples in an engaging fashion. We also present the results of a controlled study with a between-subject design that was conducted in a large introductory Python programming class to compare PCEX with non-interactive worked examples focused on program construction. The results of our study show the positive impact of interactive program construction examples on student's engagement, problem-solving performance, and learning.
“…A scaffolded self-explanation approach is structured prompting that improves the quality of self-explanations (Chen, Mitrovic & Matthews, 2019;Rittle-Johnson, Loehr & Durkin, 2017). Unlike open prompts, structured prompts include additional information such as cues for selection of appropriate explanations among various choices or fill-in-the-blanks.…”
Section: The Assisting Level Of Self-explanation Promptsmentioning
This research investigated the effects of focus (inference vs. inference followed by integration) and level (low vs. middle vs. high) in self-explanation prompts on both cognitive load and learning outcomes. To achieve this goal, a 2*3 experiment design was employed. A total of 199 South Korean high school students were randomly assigned to one of six conditions. The two-way MANOVA was used to analyse the effects of the self-explanation prompts on learning outcomes. Results showed that there was an interaction effect between focus and level of self-explanation prompts on delayed conceptual knowledge, suggesting that the focus of self-explanation prompts could be varied depending on their level. Second, learners who were given a high level of prompts scored higher on the immediate conceptual knowledge test than those who received a low level of prompts. A two-way ANOVA was conducted to analyse the effects of the self-explanation prompts on cognitive load and showed no significant interaction effect. However, there was a main effect in the level of the prompt that a high level of self-explanation prompts imposed a lower cognitive load compared to a low level of prompts. In sum, the design and development of self-explanation prompts should consider both focus and level, especially to improve complex problem-solving skills.
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