Int J Clin Exp Med 2015;8(8):14542-14552 www.ijcem.com /ISSN:1940-5901/IJCEM0011194

Original Article Research on cultivating medical students’ self-learning ability using teaching system integrated with learning analysis technology Hong Luo1,2*, Cheng Wu1*, Qian He1*, Shi-Yong Wang3*, Xiu-Qiang Ma1, Ri Wang1, Bing Li2, Jia He1 Department of Health Statistics, The Second Military Medical University, Shanghai, China; 2Center of Information Management, 3Center of Network Information, The Second Military Medical University, Shanghai, China. *Equal contributors and co-first authors. 1

Received June 8, 2015; Accepted August 4, 2015; Epub August 15, 2015; Published August 30, 2015 Abstract: Along with the advancement of information technology and the era of big data education, using learning process data to provide strategic decision-making in cultivating and improving medical students’ self-learning ability has become a trend in educational research. Educator Abuwen Toffler said once, the illiterates in the future may not be the people not able to read and write, but not capable to know how to learn. Serving as educational institutions cultivating medical students’ learning ability, colleges and universities should not only instruct specific professional knowledge and skills, but also develop medical students’ self-learning ability. In this research, we built a teaching system which can help to restore medical students’ self-learning processes and analyze their learning outcomes and behaviors. To evaluate the effectiveness of the system in supporting medical students’ self-learning, an experiment was conducted in 116 medical students from two grades. The results indicated that problems in self-learning process through this system was consistent with problems raised from traditional classroom teaching. Moreover, the experimental group (using this system) acted better than control group (using traditional classroom teaching) to some extent. Thus, this system can not only help medical students to develop their self-learning ability, but also enhances the ability of teachers to target medical students’ questions quickly, improving the efficiency of answering questions in class. Keywords: Teaching system, education data, strategic support, self-learning ability, learning analysis

Introduction With the rapid development of information technology and the rise of knowledge economy, teaching medical students how to learn, building medical students’ life-long learning systems and a learning society have become important goals in education reform and social development in any country. As an effective learning method and ability, self-learning improves learning outcomes and it is also a prerequisite and foundation for life-long learning. Pintrich defined self-learning as a self-regulating process [1]. Yu Wensen and other scholars believed that self-learning in medical students was positive but not negative [2]. Academics represented by Weiguo Pang [3] advocated defining selflearning from horizontal and longitudinal dim ensions. Previous studies suggested that self-

learning to be a learning style with medical students playing the dominant role. Self-learning indicated that the following four aspects were completed on medical students themselves: planning and goal setting; choosing and adjusting of learning content; regulating and monitoring learning process; expectation and evaluation of learning results. Many domestic and foreign researchers have realized the importance of developing selflearning ability in students and they analyzed self-learning ability from different angles, including the self-learning process, life-long learning, composition of self-learning ability in remote environment, etc [4, 5]. The researches have been carried out mainly in two ways: theoretical discussions and practical exploration. Theoretical discussions are mainly about the

A novel teaching system for cultivating self-learning ability

Figure 1. Research roadmap. R & D: how the teaching system was developed; Application: how to apply and assess the effectiveness of the system in supporting self-learning.

analysis of factors affecting self-learning, such as motivation, goal setting, attribution, etc., to explore ways of self-learning ability. Practical exploration was to explore how medical students’ self-learning ability was developed during the course of teaching, mainly focused on English disciplines. But such studies are mostly summary of experiences towards specific problems in a particular course but not applicable for all courses. Although educational institutions and teachers have recognized the importance of cultivating ability of self-learning, they are still in the exploratory stage and practical and effective teaching modes are urgently needed. Therefore, providing a platform for integrated analysis to explore ways to cultivate self-learning ability in students and to construct the corresponding teaching model is an important issue currently. In order to realize it, the “Learning Analysis” technology raised recently provided a good solution. Learning analysis was ranked as emerging technologies that had significant impact on the future of education in three to four years to come in the “Horizon Reports” of past two years

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[6-8]. It refers to “collection, analysis and reporting” data generated in certain learning situations and analysis data act as operational guidance to support and enhance the learning experience and learner success [9, 10]. In different contexts, it refers to medical students learn to collect and infer the creation of large amounts of data to evaluate learning progress, predict future performance and identify potential problems [12-14]. Data analysis and modeling help teachers and medical students to achieve teaching and learning goals and offer diagnosis for personalized learning process [14-16]. The core concept of learning analysis technology has existed for decades in computer-supported management (CMI), computer science, human-computer interaction and networking [17]. Its most notable features are providing support for the decision-making of individualized teaching [18-20], determining the needs of learners and providing the most appropriate learning materials [21-23]. However, analysis of these data cannot directly address the real problems of teachers and medical students in

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A novel teaching system for cultivating self-learning ability

Figure 2. System architecture. Number of layers, contents of each layer, functions of each layer.

specific teaching and learning process; nor can it provide solutions to specific problems. Studies for teaching and teaching meaning of the acquired data are absent [24-27]. Currently, online learning analysis technology has gradually developed into higher education [28-31]. Digital learning analysis system is widely used in the field of basic education and is developing rapidly [32]. The development of matured analysis can be used in studying key technologies, such as network analysis, discourse analysis and content analysis [33-36]. From the perspective of the assessment and improvement of the education system, leaning analysis identifies learning needs, analyzes learning mode, forecasts study results, and provides a basis for education strategies by the mining and analyzing of learning behavioral data [26, 38, 39]. From a perspective of improving assessment of learning behavior, learning analysis will aggregate the behavioral data of learning technology system and other online systems to the learner’s “E-Portfolio” [29, 39]. Mining and analyzing the behavioral data is expected to provide more effective services for learners’ personalized demands [14, 40, 41]. Methods In this study, we established a teaching system that contains instructional videos, teaching slides, electronic materials, expanding resourc-

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es, practice tests and other teaching resources corresponding to each unit of knowledge. Medical students using this system were allowed to select the desired content and teaching resources to learn. They could communicate and discuss with teachers and other students during learning. Teachers could monitor and intervene in the learning process in this system. Medical students’ learning process and test results was been fed back to their teachers in real-time. Additionally, the system had learning analyzing functions to collect and record data of medical students automatically through their learning process, thereby to establish a complete archive and analyze learning content, outcomes and other characteristics. The evaluation of the system in “Medical Statistics” indicates that it can be used as a supplement to traditional classroom teaching. This system was helpful in cultivating medical students’ self-learning ability and provided a practical method for personalized teaching and learning. The main purpose of this study was as follows: (1) Whether self-learning with this teaching system achieves the same outcome as the traditional face-to-face classroom teaching? (2) Whether learning analysis function provided by the system helps teachers to locate students’ learning problems quickly?

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A novel teaching system for cultivating self-learning ability

Figure 3. Flowchart of learning analysis. The learning analysis process; function of each process; tools used in each process; forms to show the analysis results.

This teaching system was designed with two characteristics to help develop self-learning ability in medical students. First, teaching contents was segmented to knowledge points and the digital teaching sources system consists of these knowledge points was constructed. This system was aimed to facilitate learning of medical students, assist teachers in locating specific problems quickly and to improve the efficiency of classroom question answering. It could also help to identify priorities and ways of classroom teaching [43]. Second, the learning analysis function helped to establish a “medical student-centered, teacher-led” teaching mode by tracking and monitoring real-time self-learning process [39], recording learning behavior path, providing timely feedback and evaluating medical students’ self-learning process [33], and providing subjective reference to teachers fitting teaching and personalized training. To assess the effectiveness of this system in supporting self-learning, experiments were conducted on a compulsory course “Medical Statistics” in 116 undergraduate medical students from two grades of a key medical institution in mainland China. Flowchart of this research is illustrated in Figure 1.

lection of multimedia, computers, communications equipment and other technologies. It was divided into three layers by function: the application service layer, application support layer and resources service layer (Figure 2). Application service layer: server-oriented applications directly provided users logging through a remote computer the appropriate support services, including identification, learning content selection, evaluation and feedback during the learning process, simultaneously supports for multiple users in remote learning. Application support layer: all specific application functions of the learning platform were included, such as knowledge learning, practicing self-test, teaching and learning interactions and learning process analysis. Any module can redirect and switch freely. The service layer could use any resources needed through the application support layer directly.

R&D of the system

Resource service layer: providing a variety of learning resources to different learners to meet diverse learning demands, including instructional videos, teaching slides, electronic textbooks and expanded resources. These teaching resources were dynamically updated and would accumulate as the teaching developed.

The overall design of the system: The system was built based on a local network with a col-

Functional design of learning analysis: Firstly, refine the objects of leaning analysis, using

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Int J Clin Exp Med 2015;8(8):14542-14552

A novel teaching system for cultivating self-learning ability

Figure 4. Experiment design.

case grammar framework of Fillmore, under which cognitive learning process is decomposed into learning objectives (goal), the learner (Agentive),learning content (Objective), learning paces (Locative), learning methods (Instrumental), learning outcomes (Factitive). Whereby medical students learning and cognition process can be classified into three categories: learner analysis on the “Who’s learning” (analysis of learning attitude and learning attention), learning process analysis on “what to learn and how to learn”, l (i.e., learning content and learning path), learning results analysis, so as to form a complete and clear awareness of medical student learning [30]. The main implementation of the system function: The system was developed with PHP, MySQL and Flash. PHP codes were used for specific implementations and MySQL was used for management of the platform data. System testing and deployment environment was Window 2003 Server + Apache. In addition, Dreamweaver and Photoshop software were used to improve user interface design. The whole system is based on PHP technology development, using MySQL to manage systembased data; using Ajax technology to synchronize the client and server without refreshing the interaction; using Web Service technology to 14546

complete the function of service publishing platform. Learning analytics is the core function of the system. Its main contents include: 1) medical students’ login times and durations of logging in; 2) residence time of each page (i.e. a knowledge point); 3) CTR of each page and evaluation of the knowledge level of difficulty; 4) learning content and resources viewed by medical students; 5) utilization of associated knowledge; 6) practice. In this function module, in addition to recording and analysis functions of the above general factors, it also solved two difficulty issues: (1) Recording and analyzing the switch between different teaching resources at the same knowledge point. That is when medical students learn a specific point; they may switch between different teaching resources. When the system was set for each switch, it would initiate a request to the server, thereby recording learning time of different teaching resources. When medical students clicked on another link or exit the page the learning statistics came to an end. (2) The realization of overall analysis of medical students’ learning logs. Each medical student’s learning process will generate a learning log. “Learning analysis” function in teachers end or the administrator side must be implemented for all medical students log data for data analysis. Among them,

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A novel teaching system for cultivating self-learning ability Table 1. Subjective Evaluation questionnaire Question Navigation

Options Clear Partially clear Not clear No comment Suitable for self-learning of new knowledge Convenient for knowing the acquaintance of knowledge Suitable for piecemeal learning Convenient for review No comment Fulfilled Mostly fulfilled Generally fulfilled Not fulfilled Very good Not bad Bad Teaching video Teaching slides E-textbook Expanded material Help manual Others

Knowledge presentation

Materials towards self-learning demand

Presentation of teaching resources

Most helpful teaching resource

Table 2. Test results distribution of the two knowledge types Knowledge type Conceptual content Methodical content

Group Exp. group Crl. group Exp. group Crl. group

Mean 78.59 75.73 80.37 72.04

SD 6.60 9.28 6.60 10.12

Minimum 68.00 53.00 69.40 56.00

Maximum 90.00 91.00 90.80 87.00

Interquartile Range 12.00 12.75 9.60 19.80

Figure 5. Scatter plot of test scores in two groups. A. Conceptual content; B. Methodical content. The blue dots: experimental group; red dots: the control group.

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A novel teaching system for cultivating self-learning ability Table 3. Top 3 of error rate A Knowledge point 1 2 3 4 5 6 7 8

Group Exp. Group Ctl. Group Exp. Group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group

NO. of errors (error rate%) First Second Third P5 (96.5) P2 (75.9) P1 (17.2) P5 (90.0) P2 (86.7) P1 (13.3) P7 (100.0) P3 (27.6) P7 (93.3) P3 (26.7) P1 (13.3) P5 (20.7) P4 (10.3) P8 (6.9) P5 (26.7) P8 (10.0) P9 (6.7) P32 (31.0) P2 (13.8) P1 (10.3) P32/P1 (20.0) P2 (10.0) P33 (17.2) P5 (13.8) P6 (10.3) P33 (43.3) P5 (40.0) P34/P35 (13.3) P13 (55.2) P37 (44.8) P17 (37.9) P13 (83.3) P37 (56.7) P11 (53.3) P19 (79.3) P18 (72.4) P20 (58.6) P19 (93.3) P20 (70.0) P18 (63.3) P30 (65.5) P43 (62.1) P27 (41.4) P30 (73.3) P28 (46.7) P27/P43 (43.3)

B Knowledge point 1 2 3 4 5

Group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group Exp. group Ctl. group

NO. of errors (error rate%) First Second Third P1 (82.1) P2 (57.1) P12 (35.7) P1 (93.1) P2 (55.2) P12 (27.6) P3 (35.7) P9 (28.6) P6 (21.4) P3/P6 (37.9) P9/P13 (20.7) P1 (6.9) P4 (42.9) P4/P10 (24.1) P14 (71.4) P5 (64.3) P6 (57.1) P14 (75.9) P5 (65.5) P6 (48.3) P5 (82.1) P1 (60.7) P4 (50.0) P5 (69.0) P1 (58.6) P4 (37.9)

Figure 6. Illustration of question number 17.

the knowledge evaluation and test analysis require multiple traversal [43-46] , because all

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result sets corresponding to all a triple set of data, the need for data to traverse triples to different medical students, different evaluation of the content and test data subject is calculated according to a formula to derive the appropriate statistical analysis of the results. Flowchart of learning analysis is shown in Figure 3. System application and evaluation We evaluated the system in “Medical Statistics”, a public compulsory course in medical schools, to investigate the performance of this system. The evaluation was divided into objective evaluation and subjective evaluation. The main goal of the course was to train medical students’ thinking and to master the basic statistical analysis methods. Students were expected to be able to analyze data with statistical software, understand and express statistical analysis correctly [47]. The objective evaluation system based primarily on medical students’ test scores while the subjective evaluation was reflected in medical students’ satisfaction questionnaire.

Design of objective evaluation: Two teaching experiments of objective evaluation were conducted in conceptual content and methodical content level according to characteristics of this course. According to the curriculum, “Frequency distribution table and frequency distribution diagram” and “Description of central tendency” in “chapter 2 statistical description” sections were selected

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A novel teaching system for cultivating self-learning ability Table 4. Subjective evalutaion Proportion (%) First Second Navigation Clear 51.72 76.92 Partially clear 41.38 15.38 Not clear 6.90 7.70 The presentation of knowledge in unit Convenient for knowing the acquaintance of knowledge 79.31 61.54 Suitable for self-learning of new knowledge 51.72 61.54 Convenient for review 79.31 53.85 Suitable for piecemeal leaning 65.52 38.46 The demand for self-learning Fulfilled 82.76 69.23 Partially fulfilled 17.24 30.77 The presentation of teaching resources Very good 58.62 61.54 Not bad 41.38 38.46 The most helpful teaching resource Teaching slides 44.83 76.92 Teaching video 82.76 69.23 E-textbooks/Expanded resources 37.93 38.46 Help manual 20.31 30.77 Content

Rate

as conceptual content; “chi-square test two independent sample rate” in chapter 8 “chisquare test” was selected as the methodical content. (1) Subjects and grouping: the research was divided into two separate experiments. Experiment 1 was on conceptual experiment. 59 volunteers who had never studied “Medical Statistics” courses were recruited from freshmen (grade 2013) in the major of clinical medicine (a 5-year undergraduate program). Experiment 2 was for methodical experiment. 57 volunteers were recruited from the sophomores of same major (grade 2012) who had learned this course before and were planned to learn “chi-square test”. (2) Experiment design: the participants of these two pilot studies were randomly assigned. The experimental group studied at multimedia classroom with the teaching system to learn the related content on themselves. Within 60 minutes, they could choose any teaching resources they want to learn, and the system teaching resources were provided by the same instructor in the control group. After that, a “exercise and self-test” module was provided with test time of 30 minutes. The self-learning online questionnaire was filled in after the test. The control group studied in an ordinary classroom instructed by teachers in traditional way with the same lectures as in experimental group, then they took the same test within the same time period. The questions were mainly multiple-choice ques-

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tion and true or false questions. 5-8 questions were selected from the “Medical Statistics National Excellent Library Resource Sharing Program” for each knowledge point. (3) Statistical analysis: description of data characteristic was performed with mean, median, standard deviation. Normal distribution data were compared using t-test for two independent samples while other data was compared using the Wilcoxon rank-sum test. SPSS 17.0 was used to perform data analysis, with P ≤ 0.05 considered statistically significant. Figure 4 is the process of experiment 1. Process of experiment 2 was similar but the experimental group included 28 people and the control group included 29 people. Design of subjective evaluation: The questionnaires of subjective evaluation of the system include the satisfaction of the navigation, knowledge presentation, learning materials, resources presentation, etc. Questionnaire designed as shown in Table 1. Results Objective evaluation results Analysis of learning outcomes: The distribution of the test results of the two groups of medical students in the two tests is shown in Table 2. It was shown that the average score of experimental group was higher than that of control

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A novel teaching system for cultivating self-learning ability group in both experiments, with higher minimums in the experiment group. The scores in experimental group were more centralized, which indicates that students in experimental group generally acquired knowledge better, while the scores of students in control group are uneven and their performance are not so desirable (Figure 5). Analysis of knowledge acquisition: The analysis function of the system helped teachers to know the knowledge acquisition of medical students and quickly locate problems. Table 3 shows the descending error rates of each knowledge point and lists top 3 errors and the error rates. Table 3 is results of the conceptual evaluation (A) and is the methodical evaluation (B). The “P” means title number of the question with wrong answers. The values in brackets refer to the error rate. The table shows that two groups are much alike in questions with wrong answers, especially in the number of question with wrong answers with the highest error rate is exactly the same.

grounds and levels through rigorous students selection and randomization. In each of experiments, there were a few medical students who had never experienced online self-learning. We found no difference in their learning outcomes compared with those who had experienced after analyzing their learning history and outcomes. The navigation of the system is relatively clear. It is very easy for starters, which is also confirmed in the subjective feedback of the questionnaires. Thus, this system will not only help to develop medical students’ self-learning ability, stimulate their enthusiasm and learning initiative, but will also facilitate teachers’ understanding of students’ acceptance of knowledge. Thus teachers could carry out efficient teaching methods upon specific knowledge and improve the efficiency of answering questions in class.

In addition, traditional test analysis requires teachers manually research the misunderstanding of medical students, while this system may clearly show the questions with wrong answers. For example in question 17, the teacher can locate the misunderstanding of medical students quickly (the choice D is focused), which is one of advantages of the system (Figure 6). In summary, learning problems found in self-learning and self-testing using the teaching system overall consist with the problems arisen from traditional classroom teaching.

The limitation of this research is that results of learning cannot automatically display visually in real-time. Currently, the results of learning analysis are presented as a static list in this system. Medical students can only see their leaning process and learning time of detailed learning resources and knowledge, test results and the false record. For teachers can see medical students’ overall learning records and records of terms of entire and detailed learning time of each person, sequence of learning, overall test result, accuracy and choosing time of each option. In order to stratify and transform the information mentioned above into appropriate images automatically to generate intuitive understanding of self-learning process for both medical students and teachers [48, 49], the system should be developed further.

Subjective evaluation results

Acknowledgements

The objective evaluation of application results of the system was provided. The subjective evaluation of these medical students is summarized in Table 4. Overall, about 95% of the medical students hold positive attitudes towards self-learning with this system.

This research was supported by the national fine course construction projects, the national quality resource sharing class construction projects, and evidence-based public health of Shanghai key discipline construction project.

Discussion It was necessary to consider whether there are confounding factors affecting the results of learning data collecting from the system in the evaluation. In both experiments, we tried to minimize the impact of different learning back-

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Disclosure of conflict of interest None. Address correspondence to: Dr. Jia He, Department of Health Statistics, The Second Military Medical University, No. 800 Xiangyin Road, Shanghai, China. Tel: 13386277040; E-mail: [email protected]

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Int J Clin Exp Med 2015;8(8):14542-14552

Research on cultivating medical students' self-learning ability using teaching system integrated with learning analysis technology.

Along with the advancement of information technology and the era of big data education, using learning process data to provide strategic decision-maki...
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