2.1

Machine learning

Machine learning as a multidisciplinary interdisciplinary subject, involves multiple disciplines such as statistics and probability theory and is based on research in statistics, probability theory, computer vision, and data mining. Machine learning is imitating human learning models, based on human learning models, and transferring large amounts of data to machines for simulation and drawing relevant conclusions by induction. Machine learning techniques fall into three categories: supervised learning, unsupervised learning, and reinforcement learning.

2.2

Online learning

Professor Ding (2002)⁷, a pioneer in open and distance education research in China, describes online learning as the third generation of distance education built on two-way interactive electronic information technologies (based on e-technology), and computer technology is the most important factor of distance practice. Scholars such as Huang (2012)⁸ have a broad definition of online learning, which refers to the learning method of accessing its contents through computers. In short, scholars’ understanding of the definition of online learning includes the following aspects: online learning is accessing learning materials through the Internet. Online learning is a cutting-edge approach to instructing distant learners using the Internet as a medium. Besides, Online learning has the characteristics of being learner-centered, distributed, web-based, flexible, and knowledge-building.

Online learning, also known as e-learning, is a tool that emerged in the context of the rapid development of ‘Internet + education’, which refers to the learning activities of learners in a networked environment with the help of rich and shared online learning resources. It is a new method of teaching, learning, and management in virtual classrooms through a computer network, smartphones and wireless, developed mainly by independent and collaborative learning⁹.

3.1

Main features of online learning effect evaluation

3.2

Current status of research on online learning effect evaluation

Effectively evaluating the learning effect of online learning can fairly, comprehensively, and objectively assess the quality of students’ learning and better organize the use of teaching resources. With the popularity and promotion of online learning, many researchers have proposed many good evaluation methods, and the development of online learning evaluation research has become a current hot spot. For example, Professor Zhou¹⁴, a well-known Chinese scholar, first introduced the BP algorithm into online learning evaluation in 2003. In 2008, A fuzzy comprehensive evaluation method of network learning based on entropy is proposed by Zheng¹⁵. After that, Jared and Grace¹⁶ proposed a triangular model using activity theory as an evaluation method for online learning in 2011. In the same year, Yangman¹⁷ proposed a study on a learning ability evaluation model based on web-based online learning. In 2014, Ma¹⁸ proposed a review of online learning evaluation research based on peer assessment. Lu et al.¹⁹ proposed an online evaluation model designed using an improved LMBP algorithm in 2016. Two years later, Wang and Mao²⁰ used multiple regression analysis to determine the evaluation factors of students’ learning effectiveness in online courses and constructed an evaluation model. In 2020, Yan²¹ proposed a study on the evaluation of nursing students’ online learning effectiveness. After consulting a large number of papers, it is found that there are many academic achievements of online learning effect evaluation, which are worthy of research and reference, but most of them are biased towards theoretical research such as algorithms, and research on online learning effect evaluation model is relatively few, most of them are still traditional classroom teaching evaluation.

3.3

Insufficiency of the existing online learning effect evaluation model

The existing evaluation of the online learning effect in China has the following main deficiencies. First, the evaluation system is still based on traditional classroom teaching evaluation. Due to the technical nature and insufficient functions of the online learning effect evaluation module, it makes some teachers are unable to conduct online learning evaluation well and cannot be integrated into online learning. Some colleges and universities use online learning platforms, but the functions are complicated, the effect evaluation cannot be incorporated into the teaching evaluation system on time. Second, the evaluation method is single. In the colleges and universities that lack online learning on the platform, classroom teachers can only conduct traditional classroom teaching evaluation, while colleges and universities that use online learning platforms either have a low weighting of online learning evaluation or single evaluation indexes. For example, they only evaluate the completion of assignments in the online learning platform, without being affected by factors such as the length of students’ online learning. Third, evaluation indicators are not comprehensive. Some teachers carry out the online evaluation of learning effects considering fewer evaluation indicators, such as only considering the completion of assignments in the online learning platform, emphasizing evaluation results rather than process evaluation. Teachers not only need to focus on the evaluation of students’ learning attitudes and academic performance, but also focusing on the evaluation of learning innovation and learning ability improvement, such as the quality of communication and interaction in assignments²².

Through research and analysis, it is found that Chinese learners have the following shortcomings in online learning.

3.4

Research on the evaluation index system of online learning effectiveness

The evaluation of online learning effectiveness is different from traditional learning effectiveness evaluation since online learning pays more attention to the learning status of learners and the ease of the learning process. There are two main modes of learning effectiveness evaluation of online learning, summative evaluation and formative evaluation^34,35. Summative evaluation is mainly the final evaluation of learners through online learning. For example, through tests and questionnaires. The formative evaluation mainly revolves around the evaluation methods of teacher evaluation, peer evaluation, and self-evaluation³⁶. The formative evaluation looks at course learning, homework completion, student activities, learner satisfaction, feedback, and so on.

Table 1 lists the learning effect evaluation indicators of online learning.

Table 1.

Evaluation index of learning effect of online learning.

When teachers teach online, they must evaluate online learning promptly, correct online assignments, respond to students’ information and questions, carry out online teacher-student interaction and other communication activities. Also, timely export relevant platforms data for online learning, combining the number of times students log on to online learning platforms, the length of learning courseware, the submission time of assignments, the completion of exercises to evaluation. Study in the online learning platform is an important part of the course evaluation, from ‘before class—during class—after class’ to the multi-dimensional evaluation of online learning effects, such as ‘teacher with student-student-platform’, which is more conducive for teachers to know the effect of students’ online learning in real-time, and improve the efficiency and quality of learning.

This essay aims to establish a more scientific and reasonable evaluation system for online learning, which focuses not only on the evaluation of results but also on the evaluation of the learning process. Formative evaluation and comprehensive examination of the online learning process in the middle of the learning attitude, learning process, learning resources, learning ability, learning performance, from ‘before class—during class—after class’ and other multi-dimensional assessments to evaluate the effectiveness of online learning.

According to the current teaching situation and the actual situation, it is planned to determine the indicators of the online learning evaluation system consisting of five modules: learning attitude, learning resources, learning process, learning ability, and learning performance. The specific indicators and reference scores are shown in Table 2.

Table 2.

Specific indicators and reference score Table.

The evaluation score of each first-level indicator is preliminarily determined: 20 points for learning attitude, 10 points for the learning process, learning resources and learning ability, and 50 points for academic performance. The total score is 100 points. In principle, the “final test score” of the secondary index should not be less than 30 points to ensure the importance of the final test. Teachers can appropriately increase the weight of academic performance module scores and final exam scores according to the actual teaching situation. If both online and offline learning is carried out at the same time, with classroom teaching as the main body and online learning as a supplement, teachers can adjust the scores and weights of the first and second indicators appropriately, and increase or decrease them according to their functions. The content of the first and second levels can be subtracted from the situation of the online learning platform. Teachers can dynamically analyze online learning effect evaluation indicators according to actual needs to stimulate the interest and autonomy of online learning.

To improve the learning effect and quality of online students, the index content of the online learning effect evaluation system focuses on the teacher’s evaluation of students’ online learning effect. Companies can make dynamic adjustments based on the use of online learning platforms, the courses offered by universities, and the personal needs of teachers and students, and continuously improve the functions of the learning effect evaluation module of the online learning platform to lay the foundation for the development of the platform to better evaluate the online learning effect.