Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

https://cyberleninka.org/article/n/566686.pdf

A Review on Predicting Student's Performance Using Data Mining Techniques

Encyclopedia of Information Science and Technology

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

/pdf/introductory-programming-a-systematic-literature-review-3yllvt7k18.pdf

Introductory programming: a systematic literature review

The growth of academic data size in higher education institutions increases rapidly This huge volume of data collection from many years contains hidden knowledge, which can assist the improvement of education quality and students performance Students' performance is affected by many factors In this study, the data used for data mining were students' personal data, education data, admission data, and academic data NBTree classification technique, one of data mining methods, was adopted to predict the performance of students Several experiments were performed to discover a prediction model for students' performance The class labels of students' performance were students' status in study, graduates predicates, and length of study The experiments were conducted with two-level classification, the university level and faculty level The resulted model indicated that some attributes had significant influence over students' performance

Exploration of classification using NBTree for predicting students' performance

One’s writing originality in academic world becomes more and more questionable
along with the increasingly access to others’ writings due to files archiving technology
development today, especially over the internet. Therefore, a text similarity detection
system is required. Based on that problem, this research tries to provide the solution by
developing an application with the concept of text mining which implements cosine
similarity and Smith-Waterman algorithm to detect text similarity. Cosine similarity
serves to measure text similarity based on words occurrence, while Smith-Waterman
algorithm’s function is to calculate text similarity based on words sequence. Based on
this research test result, the developed application successfully detects text similarity
from very similar to very dissimilar pair of texts.

Implementasi Cosine Similarity dan Algoritma Smith-Watermanuntuk Mendeteksi Kemiripan Teks

Due to its high failure rate, Introductory Programming has become a main concern. One of the main issues is the incapability of slow-paced students to cope up with given programming materials. This paper proposes a learning technique which utilizes pair programming to help slow-paced students on Introductory Programming; each slow-paced student is paired with a fast-paced student and the latter is encouraged to teach the former as a part of  grading system. An evaluation regarding that technique has been conducted on three undergraduate classes from an Indonesian university for the second semester of 2018. According to the evaluation, the use of pair programming may help both slow-paced and fast-paced students. Nevertheless, it may not significantly affect individual academic performance.

/pdf/utilising-pair-programming-to-enhance-the-performance-of-4d4pk74mt3.pdf

Utilising pair programming to enhance the performance of slow-paced students on introductory programming

Since several undergraduate CS students cannot understand Algorithm topic clearly due to algorithm complexity and limited class duration, several Algorithm Visualization (AV) for teaching algorithms have been developed. However, since most AV only focus on visualizing algorithm steps without mentioning why that algorithm should be chosen based on given problem, students cannot improve their understanding further than Application level (based on Bloom taxonomy). In this paper, we extend the capabilities of AV by utilizing case-based performance comparison. Case-based performance comparison aim to let students differentiate several algorithm and improve their understanding further.  Additionally, we utilize evaluation-integrated development since the main goal of an AV is not only technical functionality but also its usability. For our implementation, we implement these aspects to algorithm for solving classic problems such as 0/1 knapsack and Minimum Spanning Tree (MST) problem.

https://journal.uii.ac.id/Snati/article/download/6263/5639

Mewati Ayub

Papers

Exploration of classification using NBTree for predicting students' performance

Implementasi Cosine Similarity dan Algoritma Smith-Watermanuntuk Mendeteksi Kemiripan Teks

Utilising pair programming to enhance the performance of slow-paced students on introductory programming

Extending The Effectiveness of Algorithm Visualization with Performance Comparison through Evaluation-integrated Development

Integrating program and algorithm visualisation for learning data structure implementation