Discover Users’ Needs in e-Learning by Kano Analysis and Decision Trees

Chang

et al. 2022

Multimed Tools Appl

Self Cite

With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital learning materials have appeared in large numbers, changing people’s learning mode. In the era of information explosion, how to capture the learners’ attention to teaching videos and improve learning effectiveness is the common goal of every designer of e-leaning teaching content. Previous researches focused on the analysis of learning effectiveness and satisfaction. Instructional designers only provided design elements with high learning effectiveness or high satisfaction, and lacked in-depth analysis of the learners’ perspectives. The opinions of these e-learning users are often the key to the success of online teaching videos. Therefore, this study aims at the design elements that will be used in the teaching film. The operation mode of the piano mechanism will be employed as the content of the teaching film. Based on eight elements including arrow cueing, dynamic arrow cueing, spreading-color cueing, contrary to cueing, font style, color application, anthropomorphic, and audiovisual complementarity, we use Refined Kano Model to analyze learners’ needs of categorization of each element, and discover learners’ expectations for teaching videos. In addition, this study also conducts in-depth data analysis through decision trees algorithm, and stratification analyses using different variables (such as design expertise, using frequency, and usage experience, etc.) to find out the key design factors that affect learners’ learning. Depending on the learner’s background, the use of e-learning experience, using frequency, and the length of the learning video, our results could provide for reference when designing teaching videos. Instructional designers can better understand how to effectively use design elements, so that the teaching videos can achieve the best learning effect.

Section: Refined Kano Modelmentioning

confidence: 99%

Using refined kano model and decision trees to discover learners’ needs for teaching videos

Chang

et al. 2022

Multimed Tools Appl

Self Cite

Int. J. Emerg. Technol. Learn.

“…Predicting college students' learning requirements is an important research aspect in the mobile education management field [1][2][3][4][5][6]. Because mobile learning is a personalized and continuous training method which is free from the limitations of time and space, so it's particularly important to make accurate predictions and good plans for college students' requirements for mobile learning [7][8][9][10][11][12][13]. Initiators and subjects of the mobile learning requirements are college students who conduct learning activities to acquire knowledge of the learning targets, and the mobile learning platforms are providers of learning resources to meet the learning requirements of students [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Information Dissemination Prediction of College Students’ Learning Requirements Based on Mobile Social Network

Tong¹,

Chen²

2023

Starting point of this research is the information dissemination and fluctuation of the learning requirements of college students between the student individuals and the mobile learning platforms, to solve this problem, it’s of certain practical value to explore the information dissemination efficiency of college students’ learning requirements under different conditions and study the prediction accuracy optimization method. However, existing studies generally focus on the feasibility of applying existing methods to the prediction of college students’ learning requirements, but the research on such prediction is neither sufficient nor deep enough. This paper constructed a model of college students’ mobile social network based on the actual data of college students’ mobile social behaviors, and innovatively proposed a novel algorithm for predicting college students’ requirements for mobile learning. At last, experimental results verified the effectiveness of the constructed model and the proposed algorithm.

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

“…Meng Qingliang et al applied the kano model to improve the quality of logistics service, and believed that the application of the kano model in service attribute detection could take into account customer psychological factors and consumer motivation, which has certain advantages compared with traditional methods [5].Lu Chunfu et al used the kano model to analyze the experience needs of users of home integrated stoves and got the key points of user experience design [6].He Weishan conducted research on the design of online learning apps for the elderly based on kano model, obtained the functional needs of the elderly and ranked the importance of the demand items, thus providing guidance for the design practice of online learning apps for the elderly [7].Long-Sheng Chen used kano model to analyze users' demands for multimedia design in e-learning, better understand customers' evaluation and feedback on products, and help enterprises focus on the most important attributes to be improved [8].…”

Section: Introductionmentioning

confidence: 99%

Research on optimization design of fresh E-commerce APP based on KANO model

Zhang

2023

With the development of the Internet, online shopping has become the main choice for people to shop. Fresh goods are also gradually turning to online sales mode. Fresh e-commerce APP emerges at the historic moment. According to the user's demand for fresh e-commerce APP interview and research, use the kano model to classify the user demand attributes, and according to the user satisfaction coefficient select the fresh e-commerce APP function design practice, improve user satisfaction.kano model was used to analyze different demand priorities of fresh e-commerce APP to provide reference for functional design and optimization of fresh e-commerce APP.