Learning analytics is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. In higher educational institutes, many students have to struggle hard to complete different courses since there is no dedicated support offered to students who need special attention in the registered courses.

Machine learning techniques can be utilized for students’ grade prediction in different courses. Such techniques would help students to improve their performance based on predicted grades and would enable lecturers to identify such students who might need assistance in the courses. The emerging field of learning analytics involves the analysis of data about learners and their activities to inform the enhancement of teaching and learning practices and environment.

1. To develop a greater understanding of how students interpret and respond to online course participation through learning analysis and the influence of these online course participation on student marks and grades.

2. To collect and analyze the data about learners and their environments for the purpose of understanding and improving learning outcomes.

Our stakeholder for this project are:

1. Student

   • Students have the opportunity to enhance their performance thanks to learning analytics data and intervention; fewer learners will drop out or fail the eLearning course.

   • Students can focus on the online course learning as well as examination to ensure they pass in the course.

2. Lecturer

   • Lecturers can use this prediction to see and analyze their performance in terms of online activity that they create for an online learning course.

   • Learning analytics can help to determine if learners may benefit from supplementary eLearning materials and/or peer/instructor aid throughout the eLearning course.

   • Through learning analytics, eLearning professionals and online instructors gain the ability to custom tailor eLearning experiences for each and every individual learner.

1. What is the relationship between participation in an online course and student marks?

2. Does student participation in online courses affect their marks?

3. What are the important attributes used in predicting students' marks?

4. How students' participation in online courses can affect their grades?

5. Does the number of participants give affect students' grades?

The objective of this project is to predict student grades and CGP based on their participation in the online learning course using a machine learning algorithm.

There are several steps that involved in this project such as:

1. Introduction To Dataset

2. Exploratory Data Analysis (EDA)

3. Descriptive Analysis

4. Predictive Analysis

5. Result & Analysis

6. Streamlit Application