Hello everyone, I am Chu, a research student.

In this article, I would like to introduce the content of a paper discussed in our English Literature Seminar along with my personal thoughts.

• Paper Title: The influence of student characteristics on the use of adaptive e-learning material

• Journal: Computers & Education

• Volume & Pages: Pages 942-952

• Year of Publication: 2012

• Authors: van Seters, J.R., Ossevoort, M.A., Tramper, J., and Goedhart, M.J.

The following is an overview of the content described in the paper.

This study aims to analyze the characteristics of student groups by collecting demographic data such as gender, age, and nationality, and measuring their motivation and prior knowledge. Since European universities introduced the international bachelor’s and master’s system, the mobility of students within Europe has increased, leading to an increase in the diversity of student ages and nationalities. This study focuses on prior knowledge regarding biology, and adaptive digital learning materials are considered useful for students with diverse ages, nationalities, levels of prior knowledge, genders, and learning levels (undergraduate or master’s). Regarding the advantages of adaptive digital learning materials, several studies have reported their ability to support time-consuming tutoring and provide optimal learning paths tailored to individual needs; however, there is little empirical evidence showing that students actually follow individual learning paths based on differences in their prior knowledge. Furthermore, as a practical challenge, the development cost of Computer-Based Learning Environments (CBLE) is very high, making it extremely important to understand under what circumstances and for which students adaptive digital learning materials are effective. Research on CBLE also leads to a deeper understanding of how students employ self-regulated learning. This study specifically focuses on the Self-Regulated Learning (SRL) strategies adopted by students when using adaptive digital learning materials. Among SRL aspects, the study highlights feedback. Feedback is information generated in response to a student’s actions and is used to provide methods for students to recognize and correct mistakes or misunderstandings. This is because effective feedback can strengthen a student’s self-regulation capability, and individualized feedback is considered particularly effective.

Research Subjects The participants were students at Wageningen University. A total of 94 students participated, of whom 86 (91%) completed the pre-test, all (100%) used the adaptive digital learning materials, and 80 (85%) completed the questionnaire survey. Among the 80 participants who completed the survey, 55% were male and 45% were female. Seventy-six percent of students were enrolled in undergraduate courses, while 23% were enrolled in master’s courses. The students who completed the survey represented 12 nationalities, with 75% being Dutch. The ratio of Dutch to international students differed between the undergraduate and master’s programs; 90% of bachelor’s students were Dutch, whereas only 22% of master’s students were Dutch. Eighty-eight percent of the students were between 18 and 25 years old, and 12% were 25 years or older.

Research Purpose The purpose of this study is to investigate how individual student characteristics influence the learning paths they follow and the learning strategies they use when utilizing adaptive digital learning materials.

Research Questions The variables utilized in this study are prior knowledge, learning level, gender, and intrinsic motivation. Prior knowledge influences approaches to self-regulated learning and problem-solving, and learning level is related to prior knowledge. Gender was selected as a characteristic because it influences SRL and the use of adaptive digital learning materials. It has been reported that a student’s intrinsic motivation significantly impacts SRL (Winne, 1995). Therefore, the following Research Questions (RQ) were set:

1. Can adaptive digital learning materials be realized by students following different learning paths?

2. How do students’ prior knowledge, learning level, gender, and intrinsic motivation influence their learning paths?

3. How do students’ prior knowledge, learning level, gender, and intrinsic motivation influence the learning strategies they use?

Methods This study uses a system called Proteus, which can be optimized for practice and feedback content. To track students’ learning strategies, the concept of “step length” was proposed by Winne (2010). Step length refers to the self-selection of problem difficulty, with three steps: high (long step), medium (medium step), and low (short step). The history of problem difficulty selection becomes a trace. The tracked data represents the relationship between the selected step length and the number of required trials. A pre-test was conducted to measure students’ prior knowledge. The content of this pre-test aligned with the learning objectives of the adaptive digital learning materials. Prior knowledge was evaluated via the pre-test for the participants of this class. Evaluation was on a scale of 1 to 6, and students with 5 or 6 points were assessed as having already achieved the learning objectives. Surveys were conducted regarding the questionnaire items, learning level, gender, and intrinsic motivation (the intrinsic motivation questionnaire was modified based on the Intrinsic Motivation Inventory (McAuley et al., 1989), and its usefulness has been proven through interviews).

Results The study conducted Mann-Whitney U analysis and Spearman’s rank correlation analysis. Student characteristics, learning paths, and learning strategies were analyzed.

• Pre-test: Students’ prior knowledge was diverse, with an average score of 2.93. There were no significant differences based on gender or between undergraduate and master’s students.

• Intrinsic Motivation: The average value was 3.74; while there was no difference between genders, master’s students scored higher than bachelor’s students.

• Learning Behavior: Many students checked the original text or incorrect answers before providing an answer, and few answered by pure guessing. Some students consciously chose the step length for their learning.

• Self-reported Strategies: There was no difference between genders. Master’s students were more likely to study related theories and utilize feedback.

Discussion

• Student Characteristics and Learning Paths: Gender and prior knowledge do not influence learning paths. Undergraduate students completed tasks in a shorter time than master’s students, but this is because they selected larger steps.

• Influence of Intrinsic Motivation: Students with high intrinsic motivation select smaller steps and perform more practice, yet require fewer trials.

• Student Characteristics and Learning Strategies: Students with high prior knowledge or high intrinsic motivation consciously choose their learning steps and make greater use of information sources.

My Personal Thoughts When I first saw the title of this paper, I felt it was similar to my own research keywords. That is why I chose this paper. In this study, learning is conducted with adaptive digital materials after a survey on prior knowledge is performed. What I am considering is whether it is possible to integrate the measurement of prior knowledge with an adaptive learning environment. I believe this could significantly improve the usefulness of adaptive learning environments. This would not be limited to experimental settings but could be widely applied. However, although the concept of self-regulated learning is proposed in the paper and the Proteus system is used, there is no clear explanation of what specific learning behaviors are supported for Self-Regulated Learning (SRL). Additionally, while the literature review in the paper touches upon the concept of feedback, it does not explain in detail why feedback was used in specific adaptations, nor does it provide comparisons with other forms of support. I believe these points should be added. This paper provides useful suggestions regarding the results of coarse-grained data when measuring student abilities. Although I plan to handle fine-grained data in my own research, I believe this work is positioned as an important preceding study for my research.