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Slow Learners in Education: An Annotated Bibliography

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Slow Learners in Education: An Annotated Bibliography

Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and prediction-based data mining algorithms to predict slow learners in the education sector. Procedia Computer Science, 57, 500-508.

The authors of this journal think that the educational Data Mining field concentrates mostly on prediction instead of setting their eyes on the results which could be used in the future. Therefore, regular analysis is a must of educational databases to check on the changes occurring in curriculum patterns. This study's objective was to display using classification-based algorithms, a predictive model, after identifying slow learners among other students. To achieve this goal, the authors used the Real-World data set taken from a high school. WEKA, an Open Source Tool, was used to carry out desired potential variables' desired filtration. There is testing the dataset of student academic records and applying on various classification algorithms such as Naïve Bayes, Multilayer Perception, J48, SMO, and REPTree using WEKA, an Open-source tool. Comparing all five classifiers after the generation of the statistics is used to the accuracy and finding the best performing classification algorithm among all based on classification algorithms. Besides, there is a knowledge flow model shown by the authors in this model. The paper is very important for study because it showcases the importance of Prediction and Classification-based data mining algorithms in education and presents some promising future lines concerning slow learners.

Vasudevan, A. (2017). Slow learners-causes, problems, and educational programs. International Journal of Applied Research, 3(12), 308-313.

In this journal, the author suggests that slow learners should not be classified as special cases that will need special attention. That is why they need extra time so that they can catch up with other learners. The authors believe that slow learners can like other average students. They define a slow learner as the one who is slower than an average student. Furthermore, the author identifies illness, absence from school, and sometimes low intellectual capability as some of the reasons for slow learning. Using the qualitative method, the authors concluded that environmental factors also contribute to this slow learning. They think that teachers should identify slow learners in time for them to be helped and not taken at the average learners' pace. They also found that slow learners are normally very effective with a changeful designed step since they can learn if the instruction is approached changefully. They conclude that the ways in this reigned are remedial instruction and tutoring.

Tran, T., Nguyen, T. T. T., Le, T. T. T., & Phan, T. A. (2020). Slow learners in mathematics classes: the experience of Vietnamese primary education. Education 3-13, 48(5), 580-596.

This study was conducted to identify and support slow learners in learning mathematics at primary schools in Vietnam. This journal's author suggests that it cannot talk about learning without talking about the educational system. They are two realities that are closely linked and that condition each other. A first element, certainly problematic, is in the so-called slow learning, and it is problematic because it defines an ideal speed parameter, but strictly referring to that education system. Much of the educational systems in the world are rigidly standardized. In other words, they define what each person must learn, how, and when to do it. They also define specific ways to assess whether or not this was achieved. They suggest that to help slow learners, and there is a need to understand the slow learners' cognitive and behavioral characteristics. The study took three years, and the authors reported the findings. They concluded that there is a need for individualized studies for slow learners because there are both home- and school-based barriers to slow-learning students' academic success.

Kumar, M., Shambhu, S., & Aggarwal, P. (2016). Recognition of slow learners using classification data mining techniques. Imperial journal of interdisciplinary research, 2(12), 741-747.

The authors of this study are of the feeling that educational data mining can be used to make predictions of students' learning behavior. They further go ahead to say that the Intelligent Tutor System for Linear Algebra can be used in the student's education and training as a support to the teaching-learning process on the topic of Diagonalization. With the objective of the work accomplished, the field of Intelligent Tutor Systems is provided with this new tool results in again, not only for the performance of the slow student learners only but also for the student, who is the fundamental human component that makes the system useful and gives it an identity. Thus, it is intended to contribute by suggesting grouping techniques such as the Farthest First and classification techniques such as Naive Bayes to facilitate selecting the appropriate pedagogical protocol. With its implementation, more efficient use of the material and human resources is predicted and the search for methods and solutions for a better understanding of the subject.

Shoaib, M., Inamullah, H. M., Irshadullah, H. M., & Ali, R. (2016). Effect of PQ4R Strategy on Slow Learners' Level of Attention in English Subject at Secondary Level. Journal of Research & Reflections in Education (JRRE), 10(2).

The authors were of the view that learning a language is a complex process. For some people, this occurs without major obstacles. However, others do not perform well, and their learning encounters multiple difficulties. PQ4R is an acronym that stands for Preview, Question, Read, Reflect, Recite, and Review. This is a student's centered learning strategy. Several researchers in ​​foreign language learning have identified that factors such as motivation, anxiety, learning styles, and study strategies are key in the development of learning. The present investigation explored the incidence of motivational factors, anxiety, learning styles, and strategies in the slow and difficult learning of English observed in some students of the Icesi University. We worked with ten students from Icesi University. They have difficulties learning English as a foreign language, investigating how they were perceived in these areas through focus groups and in-depth interviews. Questionnaires that measure these constructs were also applied: Gardner's AMTB, Horwitz's FLAS, Oxford's SILL, and Felder-Silverman's ILSQ. These questionnaires were also administered to ten students who performed well in English. In their conclusion, the PQ4R strategy proved to be effective in increasing slow learners' level of attention.