Clustering-based Analysis of Turkiye Student Evaluation Dataset

Student evaluations are an essential tool for assessing the quality of teaching and learning in educational institutions. However, analyzing the vast amount of evaluation data collected can be challenging, requiring manual effort and expertise. Machine learning algorithms, such as clustering, can provide a solution by automatically grouping similar evaluations and identifying patterns and trends.

In this project, we aim to use clustering-based machine learning techniques to analyze and understand the Turkiye Student Evaluation dataset. The dataset contains evaluations of instructors and courses from 5820 students from 33 different departments at a university in Turkey. The dataset includes a range of features, such as course material, instructor’s expertise, and overall satisfaction.

The proposed workflow for the Turkiye Student Evaluation Analysis project includes the following steps:

1. Data Collection and Preprocessing: We will collect the Turkiye Student Evaluation dataset and preprocess it by cleaning, transforming, and normalizing the data. We will also perform exploratory data analysis (EDA) to gain insights into the data and identify any missing or inconsistent values.
2. Feature Selection and Engineering: We will select a subset of relevant features from the dataset, such as course content, instructor’s qualifications, and student feedback. We will also engineer new features by combining or transforming existing features to improve the clustering performance.
3. Model Training and Selection: We will train a set of clustering algorithms, such as k-means, hierarchical clustering, and density-based clustering, on the preprocessed dataset. We will evaluate the performance of each algorithm using metrics such as silhouette score, homogeneity, and completeness and select the best-performing algorithm.
4. Cluster Analysis and Interpretation: We will analyze the resulting clusters to identify the most representative evaluations, features, and patterns. We will also visualize the clusters using interactive plots, heatmaps, and other graphical tools to facilitate human interpretation.
5. Application and Visualization: We will apply the clustering algorithm to new, unseen evaluations to classify them into the existing clusters. We will also visualize the results using interactive dashboards, which will enable users to explore and analyze the evaluation data interactively.

The expected outcomes of this project include a scalable and efficient clustering algorithm for analyzing the Turkiye Student Evaluation dataset, a comprehensive set of visualizations and insights into the evaluation data, and a set of best practices and guidelines for applying clustering algorithms to educational evaluation data. The project has numerous applications, including course and instructor evaluation, quality assurance, and educational research.