Abstract
With the development of education informatization, smart devices are being widely used in teaching, which has triggered significant changes in higher education. College students’ learning reflects the cognitive characteristics of digital natives, but their learning attitude and motivation have not significantly improved. Meanwhile, learning assessment in higher education is mostly based on the traditional assessment system, which has only a single index, a simple process, and mundane goals for learning assessment, thus making it difficult to effectively promote students’ learning. The present study, therefore, constructed an assessment index system using a pressure–state–response (PSR) model, gathered data using a questionnaire, and processed the data using SPSS and Amos. The results suggest that learning assessment in higher education should use a process assessment strategy and that the assessment system should be composed of three parts: classroom assessment, after-class assessment, and assessment evaluation. This type of all-round assessment can serve to effectively promote students’ learning. In addition, assessment evaluation should focus on students’ goals regarding their ability to innovate and reduce the weight of goals related to knowledge and skills while also giving teachers more adjudication power in assessment.
1. Introduction
Phenomena such as “blitz before exams” and “hooray for 60 points” remain common in Chinese higher education, and many college students are not sufficiently motivated to study. This is related to the unreasonable and unitary nature of the learning assessment index system in China. The basic principle for higher education learning assessment is to promote learning [1, 2]. With the development of education informatization, the learning environment, learning objectives, and learning styles are all undergoing major changes [3]. Thus, the learning-promotion function of traditional assessment is being weakened [4], creating a need for innovation and improvement.
Education informatization has altered the style of higher education [5], and information technology devices are now widely used in both academia and everyday life. This has profoundly influenced the way college students think, lending them the cognitive learning characteristics of digital natives.
Improving the learning assessment model and implementing whole-process assessment are highly recommended. Traditional learning assessment is excessively focused on final assessment and neglects the assessment of students’ performance in and out of class. For example, some students might lack a serious attitude about studying, and their completed homework might not be of high quality, but they might nevertheless get relatively good scores in their learning assessment by means of cramming, which seriously affects their initiative and motivation in learning. Meanwhile, schools should increase the weight of learning assessment in students’ merit evaluation and priority selection, thereby enhancing the motivational function of learning assessment. Reform the assessment model, and pay more attention to the assessment of ability objectives. The questionnaire indicated that many students are afraid of examinations, which is related to not only students’ own motivation but also the content of the examinations. This phenomenon is mainly reflected in the following aspects.
1.1. Quick Learning Characteristics
The shortcut features of smart devices have affected the learning mindset of college students. Students widely use features such as copy, paste, and other simple editing works. Textbook plus mobile phone is now the standard configuration for classroom learning, and taking pictures with mobile phones has replaced pens and notebooks. All of these are characterized by “quickness.”
1.2. Dependent Learning Characteristics
Being constantly in a school or home environment, learners have naturally established a type of directed thinking, relying on teachers and media for learning. This learning dependence is manifested by the fact that students generally do not read books without teachers’ guidance. Moreover, they will not do homework without reference templates or do experiments without ready-made materials.
1.3. Passive Learning Characteristics
Overall, China’s education system is still examination oriented. As a result, learners are accustomed to being taught and inculcated, and passive learning remains the preferred method among college students. Students’ learning initiative is generally not high, mainly manifested by the fact that books other than textbooks are seldom covered, before-class prelearning is mostly absent, and discussion-based learning is difficult to establish.
1.4. Utilitarian Learning Characteristics
Learning objectives are biased in favor of practical interests, such as employment, graduate school admission, and salaries, often ignoring long-term goals related to professional learning. Learning processes focus on acquiring skills and operational knowledge but generally undervalue research-based knowledge. Extracurricular learning prioritizes clubs or competitions relevant to evaluation, merit, priorities, and awards. Meanwhile, projects unrelated to quantitative assessment are downplayed.
There is also a prominent phenomenon of high scores and low ability in higher education. This indicates that the current learning assessment system is unscientific and does not reflect actual learning. Although the learning styles of digital natives expand their horizons and resources, their thinking skills are affected in ways that might be unfavorable to the achievement of innovative goals. Globalization has greatly increased international competition in higher education, and countries around the world are implementing innovative development strategies. Knowledge and skills are no longer the main goals of higher education; instead, innovation capacity has become the most important indicator of educational. Traditional learning assessment advocates standardized testing. As a result, many colleges and universities have implemented a system that separates teaching and testing, mostly focusing on acquiring knowledge and skills, with learning goals related to innovation and thinking often ignored.
To enhance assessment’s operability and simplify its process, the traditional learning assessment system generally focuses on summative assessment [6], such as curricular examinations and thesis projects, while paying relatively little attention to process assessments, such as students’ in-class and after-class performance. This can greatly reduce the accuracy of assessment. Learning is a long-term process. Learning assessment should likewise follow the principle of process assessment and pay more attention to students’ classroom performance and after-class performance, thereby improving assessment accuracy.
2. Study Design and Implementation
What type of assessment system can effectively promote learning and facilitate the cultivation of students’ innovation capacity? This should be the main question addressed in the design of learning assessment in higher education [7]. Students are the main body of learning, and the design of a learning assessment system should therefore mainly follow learners’ opinions and ideas [8]. Meanwhile, teachers should be guides for learning assessment design. Learning assessment is an activity of value judgment [9], and the accuracy of judgment is rooted in the index system. Therefore, the design of the indices is very important. In terms of the characteristics of higher education learning, the learning assessment system should have the following features:
2.1. Learning Assessment Is an Open and Inclusive System That Can Adopt the Internationally Used Pressure–State–Response (PSR) Model
The PSR model was proposed by the Organization for Economic Cooperation and Development (OECD) and is widely used in designing assessment index systems [10]. The learning objectives are to achieve the comprehensive, coordinated development of knowledge, skills, and abilities (KSA) [11], which requires students to invest considerable time and effort, thus creating pressure (P). State (S) reflects changes in knowledge, skills, and abilities during the learning period and is the result of pressure (P), as well as the motivation performance of response (R), as shown in Figure 1.
2.2. Students Are the Main Body of Learning [12], and the Design of Learning Assessment Indices Should Pay Attention to their Opinions
At the end of October 2021, Assessment for Learning Questionnaire for Higher Education was conducted, mainly among students at the Sichuan University of Arts and Sciences. This university currently has more than 10,000 students (). According to the formula for sample size, , is usually set to 0.5 to derive a plausible sample size [13], and the confidence level is set to 0.05 and to 1.96. Thus, we determined . The number of valid responses to the survey was 373, and the total number of respondents was 432. Thus, the valid percentage was 86.34%, in line with the survey requirements.
2.3. The Assessment Indices Constitute a Quantifiable System, Completely Using Quantitative Assessment
The questionnaire was of the closed type, using a five-point Likert scale (from 5 = “very necessary” to 1 = “very unnecessary”) [14]. The quantitative indices were converted on a five-point scale in terms of order of magnitude (e.g., the number of homework assignments).
2.4. Learning Is a Long-Term, Complex Process, and Learning Assessment Should Therefore Be a Process Assessment [15]
Learning is more about process and method than outcome. As Lao Tzu famously said, “It is better to teach a man to fish than to give him fish.” Likewise, learning assessment should also be a long-term process, and short-term assessment is unlikely to provide accurate results. Process assessment is needed to scientifically ensure the quality of assessment [16].
As shown in Table 1, in addition to the background variables, the questionnaire collected a wide range of indices for process assessment, and a total of 32 initial assessment items were designed [17, 18]. Indices with an average score below 3.5 on the Likert scale were eliminated. Those above 3.5 were regarded as being recognized by more students. On that basis, 14 items were selected as the assessment indices. Based on general suggestions by expert teachers, theory examinations and research projects were incorporated into the assessment system as well, resulting in a 16-item subquestionnaire assessment system.
Based on the testing data, quality analysis was performed for the subquestionnaire, and item analysis and exploratory factor analysis were used to establish the basic structure in terms of the three dimensions of the questionnaire (i.e., KSA), as shown in Table 2.
Validity factor analysis was conducted using Amos 26 to obtain the questionnaire results. Table 3 shows the fitting index results of the first-order three-factor validation model of the learning assessment for students’ development. (i)For the whole model, and ; significance probability was , . This suggested that the null hypothesis is accepted. Hence, there was a satisfactory degree of fitting between the theoretical model and the data. Among the incremental fit indices, satisfactory results were obtained for RFI (request for information), CFI (comparative fit index), NFI (normalized mutual information), IFI (Internet finance international), and TLI (Tucker-Lewis index).
Figure 2 shows the results of the multifactor oblique model of the subquestionnaire. Among the three dimensions of knowledge, skills, and abilities, the correlation coefficients ranged from 0.78 to 0.92, indicating a strong correlation among the dimensions, which is in accordance with objective reality. The factor loadings between each observation value and each factor dimension ranged from 0.50 to 0.83, none of which is below 0.45. The lowest SMC value was 0.25, which is still greater than 0.2. Therefore, the structural validity of the subquestionnaire was good.
A reliability test was conducted for the questionnaire to determine its basic structure. As shown in Table 4, the coefficient and Guttman split-half coefficient were both greater than 0.7, with the majority being above 0.8, thus reaching an excellent level.
3. Results and Discussion
The ultimate goal of learning assessment is to promote students’ KSA development. The relative weights of the three elements of KSA have changed over time, and the importance of abilities (A) should be highlighted [19]. Using Mufti–analytic hierarchy process analysis, the weight coefficients of the primary KSA indices and those of the secondary indices were derived. Then, the weight coefficients of each factor were obtained by calculating the primary and secondary coefficients, as shown in Table 5. The CI value calculated for the sixth-order judgment matrix was 0.000, and the RI value was 1.260, based on checking the table. Thus, the calculated CR value was 0.000, meaning that the judgment matrix satisfied the consistency test, and the calculated weights were consistent, as shown in Table 6.
Taking into account the implementation of innovation development strategies in all countries around the world, the weight of A can be increased to 40%, and those of K and S are 30% and 30%, respectively.
The following countermeasures and recommendations are proposed: (1)Since the operation of the KSA learning assessment is relatively cumbersome, to facilitate teachers’ assessment, we propose implementing the three-dimensional components of classroom performance assessment (30% weight), after-class performance assessment (30% weight), and examination assessment (40% weight) [20]. The specific tasks are distributed as follows. Each secondary index has roughly comparable weight, and teachers can score students in a comprehensive manner based on their classroom performance, after-class performance, and examination performance. This approach not only achieves multifactor assessment but also has excellent operability, as shown in Table 5(2)Improving the learning assessment model and implementing whole-process assessment are highly recommended. Traditional learning assessment is excessively focused on final assessment and neglects the assessment of students’ performance in and out of class. For example, some students might lack a serious attitude about studying, and their completed homework might not be of high quality, but they might nevertheless get relatively good scores in their learning assessment by means of cramming, which seriously affects their initiative and motivation in learning. Meanwhile, schools should increase the weight of learning assessment in students’ merit evaluation and priority selection, thereby enhancing the motivational function of learning assessment(3)Reform the assessment model, and pay more attention to the assessment of ability objectives. The questionnaire indicated that many students are afraid of examinations, which is related to not only students’ own motivation but also the content of the examinations
Data Availability
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Conflicts of Interest
The authors declared that they have no conflicts of interest regarding this work.
Acknowledgments
This study is financed by the Sichuan Education Department, the New Development Trend of Educational Technology based on ASSURE model, code kt20210923e8aa004.