Online Course User Engagement Data

User Interaction and Performance in Online Courses

By JIFAN LI [source]

About this dataset

> This dataset offers a comprehensive and in-depth record of user engagement and enrolment in online courses. Compiled by JIFAN LI, the dataset encompasses intricate details concerning the way users are interacting with different virtual courses and platforms. It incorporates demographic details of users along with their specific activity metrics and performance indicators. > > The variables included in this rich set yield insights about gender preferences, the number of active days on course platforms, interaction volume with course content (chapters), participants' year of birth, total forum posts made during the course duration, and an individual's level of education. > > One significant aspect is how meticulously this colossal data collection work has been completed. Apart from such standard information like user's country or location, whether they have viewed or explored the course or not; it even caters to intricate nuances like tracking if a user has registered for a course or completed it which paints an accurate picture about dropout rates. > > Further delving into its contents reveals columns such as ‘final_cc_cname_DI’ that tags each participant according to their respective countries whereas 'LoE_DI' details out their education level. The 'grade' provides insights into each candidate’s final score in their chosen courses while 'start_time_DI' pinpoints exactly when one began attending these programs. > > Other vital data points covered include identifying if a student is certified or not (column name: ‘certified’) along with tracking his/her interaction volume through parameters such as 'nevents', 'ndays_act', 'nchapters', capturing respectively the count of events including interactions one had with all program sections -from scholar sessions to practical classes-, total active day counts on online learning platforms since registration/admission date till last sign off date (or recorded date) from this platform, also following up on how many chapters did they navigate through during their entire journey. > > Furthermore,'explored','nforum_posts' offer detail orientation about students’ dedication vis-a-vis courses. The ‘roles’ feature distinguishes an individual's capacity - as a student, instructor etc. within a course and the engagement dynamics therein. > > In order to capture anomalies or students facing hiccups with their digital learning journey, we have the 'incomplete_flag' that identifies those students who haven't completed their courses for some reason or another which can be an invaluable data point encouraging institutions to take corrective action. Yet another unique value point is ‘Random’ assigned uniquely to each student that works as an alternative identifier in analyzing patterns without risking personal identification leaks. > > This dataset provides a holistic 360-degree overhead view on the dynamics of

How to use the dataset

> > This detailed dataset contains specifics about individual user interactions with an online course. It covers demographics, enrollment status, activity metrics in the course, performance indicators like grade and certification status, and many more parameters. Using these data points, one can generate insights into student behaviors and preferences in online learning environments. > > Here’s how you could leverage different parts of this data: > > - User Demographics: The fields 'final_cc_cname_DI', 'gender', 'YoB' & 'LoE_DI' provide demographic details about the user such as country of origin, gender identification,the birth year which helps us calculate their age at the time of enrolment and their level of education at the start of the course respectively. > > - Engagement Metrics: The fields ‘nevents’, ‘ndays_act’, ‘nchapters’ & ’nforum_posts’ give details about a user’s engagement with the course i.e., total number of interactions a student had with the bit platform (clicks), number of unique days a student interacted on platform,number out of 20 chapters viewed by students,number for forum posts each user has posted.number > > - Course Performance Metrics: The fields 'grade','explored' ,'viewed',& 'certified' provide information on how well they did in terms course completion,in terms if grade obtained – if they explored deep into material ,recieved certificates for completing courses etc . These parameters can be used to evaluate overall users performance over multiple courses or even to asses users interest and engagement towards specific kind/group/field type courses. > > - Course Registration Status: The fields ‘registered’ tell whether a student registered or not . > > - Instructor related details - Another important aspect would be instructors rated feedback or behaviour which influences user's decision ,learning capabilties slightly.The columns like roles return such details which could be used in investigating n mode detailed aspects of not only student behaviour but also to investigate other impacting influences onto the user. > > - User Identification: The 'random' field can be used as a unique identifier for each student, if one is studying individual behaviors or tracking performance over multiple courses etc. > > Some possible data exploration paths could include comparing demographic factors with course performance indicators, examining the impact of engagement metrics on certification rate across different education levels, or simply looking at how many students from a certain country tend to engage with their chosen online courses. > > Remember that these data provide a snapshot of individual’s journey with their

Research Ideas

> - Personalized Learning Recommendations: This dataset can be used to analyze user behavior and performance in various courses, which can later help in recommending personalized learning paths to learners based on their profiles, engagement level, and past performances. > - Retention and Engagement Analysis: By understanding student's activity levels (like interactions with the course, active days), course completion rates, exploration of different materials etc., educators or platforms can identify patterns that lead to higher retention and engagement and accordingly tweak or design their courses. > - Predictive Analytics for Success Rate: With demographics data along with course interaction metrics like number of posts made in forums, number of chapters interacted with etc., machine learning models could be built for predicting a student's chance of successfully completing a given course. These predictive insights could assist learners in focusing on key areas that will likely increase their success rate. > > - Gender-based Study Patterns Analysis: Analyzing gender-specific data may reveal interesting insights about study patterns between males and females (e.g., frequency/duration/course types). Better understanding these differences might guide the development of more inclusive educational content. > - Understanding Impact of Age & Education Level on Online Learning: The dataset contains attributes like year of birth & level-of-education which might give interesting insights related to the aptitude/ability/success-rate/preferences of different age groups or individuals having different educational qualifications when it comes to online learning. > > - Course Design Improvements: The details about each user’s interaction across various chapters combined with final grade they receive may give hints towards which parts/chapters/content within courses are faring well/poorly thereby providing opportunity for improving content involved within those sections/chapters/courses

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > See the dataset description for more information.

Columns

File: Courses.csv

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit JIFAN LI.