AI Education in Massive Open Online Courses: A Content Analysis

Computer Assisted Learning

et al. 2022

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Background: Group size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem-solving process.Objectives: This study investigated the effect of group size in a K-12 introductory Artificial Intelligence course by comparing the students' cognitive load, learning motivation, collaborative problem-solving quality, and in-classroom interaction between two-and three-student groups.Methods: Forty-eight high school students were randomly assigned to two kinds of groups (i.e., the two-student group, and the three-student group, each consisting of 24 students). During the experiment, Xiaofei robots were used to teach the theoretical and practical content of five AI topics over 6 weeks for 1.5 h each week. Results:The ANOVA results indicated that group size mattered in the AI course, the two-student group was more effective in terms of enhancing students' learning motivation and collaborative problem-solving quality, as well as imposing more cognitive load than the three-student group. The advantage was more obvious in the practical problem context. The Lag Sequential Analysis results indicated that more collaborative learning behavioural sequences existed in the two-student group than in the three-student groups.Contribution: This research provides empirical evidence and potential guidance for group design in Artificial Intelligence Education. Although group size did not affect students' learning achievement, it affected learning motivation, cognitive load, and problem-solving quality and processes. Two-student groups work better than three-student groups in the AI course.

Section: Literature Reviewmentioning

confidence: 99%

Effect of groups size on students' learning achievement, motivation, cognitive load, collaborative problem‐solving quality, and in‐class interaction in an introductory AI course

Computer Assisted Learning

et al. 2022

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“…For example, it enables students to discover real engineering product problems (David and Larry, 2001) and seek solutions from the observed issues (Romero-Saritama, 2019), which is a learning outcome that meets social needs instead of simply memorizing basic knowledge (Hidayat, 2017). Besides, PBP was regarded as an active learning strategy (Rosales-Torres et al, 2020) and was reported to be effective in promoting student's creativity (Kaufman et al, 2017;Widyastuti and Utami, 2018;Zhou et al, 2021), innovative skill development (Cannon and Leifer, 2001), and learning performance (Jeprimansyah et al, 2018). Mardin et al (2018) argued that PBP was conductive to increase students' learning achievement, involving the internalization of knowledge, skills, affections, and competencies through structured processes in science and technology education.…”

Section: Effect Of Product-based Pedagogy On Learning Outcomesmentioning

confidence: 99%

“…In recent years, the rapid development and the disruptive potential of artificial intelligence (AI) are a global trend and call on innovative talents in the future society. Various countries have issued corresponding education policies to actively welcome the opportunities and challenges brought by AI technologies and prepare for the arrival of the new era (Zhou et al, 2021). Despite this, there is a limited discussion of pedagogical approaches utilized in high school that are applicable to teaching AI courses.…”

Section: Introductionmentioning

confidence: 99%

Effect of product-based pedagogy on students’ project management skills, learning achievement, creativity, and innovative thinking in a high-school artificial intelligence course

Shen²,

Lin³

2022

Front. Psychol.

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The purpose of this study is to explore the effectiveness of product-based pedagogy (PBP) on students’ creativity and innovative thinking in artificial intelligence (AI) education. A seven-step model (i.e., phenomenon, problem, plan, prototype, product, presentation, price) in accordance with PBP was proposed, in which the key function of the product as a linkage between creativity and innovation was emphasized. A total of 209 students from a major high school in South China were randomly assigned to a treatment group with PBP and a control group with direct instruction. Results indicated no significant difference was found in students’ learning performance; however, students in the treatment group performed significantly better than the control group in terms of students’ project management skills, creativity, and innovative thinking. This research validates the feasibility and effectiveness of the PBP and highlights its advantages for high-school AI education, which indicates a new direction for cultivating creative and innovative talents.

“…The MOOC was born at a time when the concept of open education was widely accepted (Zhan et al , 2015). As an important way to solve the lack of teachers and teaching resources (Zhou et al , 2021), MOOC is important to raise people’s awareness of lifelong learning and promote the building of a learning society.…”

Section: Introductionmentioning

confidence: 99%

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Tong

2023

ITSE

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Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.