The purpose of this paper is to mine or detect meaningful learning patterns for profiling high-achieving students using e-book-based activity logs and questionnaire. The analysis of this study uses association analysis with Apriori algorithm. Logs for this analysis were collected from 99 first-year students who use a document viewer system called BookLooper, questionnaires and Moodle in an information science course at Kyushu University. From the results of the association analysis, we found that high-achieving students and BookLooer have significant relationships in terms of preparation and review time. This paper believes that the profiling and analysis can be used to predict their final grades and to detect effective learning patterns.
Logs for this analysis were collected from 99 first-year students via BookLooper and Moodle. These students took an information science course in the second semester of the 2014/2015 school year at the Kyushu University. The number of logs are collected approximately 330,000. We use Moodle to manage students’ attendance, mid-semester test score, end-of-term test score, and report score. Also, BookLooper is used for collecting students’ operation logs and three types of learning time of each student: Preparation Time Before Class (BTBC), Learning Time During Class (LTDC), and Review Time After Class (RTAC) using Booklooper for profiling the relationships among high-achieving students, BTBC, LTDC and RTAC because students who devoted much time to prepare and review are not necessarily good score. In addition, it is important to categorize them efficiently in order to detect or mine meaningful learning patterns for profiling high-achieving students. Therefore, we divide numerical data such as the number of attendance, lateness and absence, report scores, mid-semester test scores, end-of-term test scores, three types of learning time and final score to several categories excluding numerical data. This paper establishes criteria for categorizing them as shown in Table 1. The high-achieving students of the top 20 percent mean A rank. For example, if a student devoted much time more than 2364 seconds in order to prepare the content by the next lesson using BookLooper, we categorize the student to “BTBC = A rank”.
The students were required to answer questionnaires before class in order to investigate their life styles, a method and time of transportation to university, the amount of learning for one day, and satisfaction of university life. Table 2 shows the questionnaires. Q1 and Q2 ask about their life style in the morning such as breakfast and time to get up because the class of the information science course starts in the morning. Q3 and Q4 ask about their commuting method and time in order to analyze relationships among high-achieving students, commuting method and time. Q5 asks about the amount of their study time for one day. Q6 asks about satisfaction of their university life. In the next section, this paper describes how to mine meaningful learning Attendance (Scorning 30) >= 23
Report (Scoring 40) >= 35
Mid-semester (Scoring 10) >= 9
End-of-term (Scoring 20) >=16 patterns for profiling high-achieving students using these data as described Sections titled “Categorization of academic achievement” and “Questionnaires”.
In order to mine meaningful learning patterns for profiling high-achieving students, this paper uses an
association analysis with Apriori algorithm. Association analysis is one of the popular analysis methods in order
to mine regularities between some parameters of educational big data. For example, Mouri et al.
In order to find the relationships between high-achieving students and the effectiveness of BookLooper, and high-achieving students and the questionnaires as shown in Table 2, this paper investigates the association rules that the conclusion parts are “report score is rank A”, “mid-semester test score is rank A”, “end-of-term test score is rank A” and “final score is rank A”. We found important some association rules shown in Table 3.
The rules from 1 to 5 show that the conclusion part is report score A rank. The “BTBC=A” of the rule 1 means that students devoted much time more than 2364 seconds in order to prepare by the next lesson. The relationships between “BTBC=A” and high-achieving students have a high relativity because the confidence value of the rule 1 is 1. The rule 2 and 5 show that the condition parts are “Q1= (3) && Q4= (1)” and “Q2= (1) && Q4= (1)”. This means the commuting time of high-achieving students to university is less than 30 minutes. In addition, they get up early in the morning and eat breakfast every day.
The rules from 6 to 10 show that the conclusion part is mid-semester test score A rank. The rule 6 and 8 show that the condition parts are “attendance = A && report=A” and “attendance = A && Q1= (3)”. In order to achieve the mid-semester test score A, it indicates that it is important to get attendance sore more than 23 points. The rule 9 and 10 shows that the relationships between “report=A && Q4= (1)” and mid-semester test score, and “report=A && Q2= (1)” and mid-semester test score.
The rules from 11 to 15 show that the conclusion part is end-of- term test score A rank. The “LTDC =A” of the rule 12 means that students devoted much time more than 32025 seconds using BookLooper during class. In addition, the “RTAC = A” of the rule 13 means that students devoted much time more than 10718 seconds using BookLooper in order to review the content after class. That means that it is important to achieve the conditions of "RTAC=A" and "LTDC=A" if students want to get the end-of-term test score A rank.
The rules from 16 to 20 show that the conclusion part is final score A rank. The rule 16 and 17 means that the condition part is “BTBC=A” and “RTAC=A”. That means that it is important to achieve the two conditions if students want to get final score A rank. Conversely, if students have “BTBC=E” or “LTDC=E”, Most of them got final score E rank as shown in Figure 2. Therefore, there is a possibility that the profiling highachieving students lead to discoveries of students who fail to make the grade.
This paper describes how to mine or detect meaningful learning patterns for profiling high-achieving students
using e-book-based activity logs. In order to mine the learning patterns, this paper uses association analysis
with Appriori algorithm. The analysis was conducted to find the relationships between high-achieving students
and the effectiveness of a document viewer system called BookLooper as shown in Table 2, and high-achieving
students and the questionnaires as shown in Table 3. In addition, this paper investigated the association rules
that the conclusion parts are “report score is rank A”, “mid-semester test score is rank A”, “end-of-term test
score is rank A” and “final score is rank A”. In the future, we will consider supporting students who fail to make
the grade using the detected association rules. Also, we will consider visualizing various methods such as social
network analysis
This part of this research was supported by the Grant-in-Aid for Scientific Research No.25282059, No.26560122, No.25540091 and No.26350319 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. The research results have been partly achieved by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” (178A03), the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan.