RTC403 Instructional Data Analytics and Education Intelligence

Code RTC403
Name Instructional Data Analytics and Education Intelligence
Status Compulsory/Courses of Limited Choice
Level and type Post-graduate Studies, Academic
Field of study Computer Science
Faculty
Academic staff Atis Kapenieks, Žanis Timšāns, Bruno Žuga, Kristaps Kapenieks, Viktors Zagorskis, Ieva Vītoliņa
Credit points 5.0 (7.5 ECTS)
Parts 1
Annotation On a large scale, continuous learning content analysis, design, development, integration, and testing identify instructional design practices linked to (1) business, (2) research and (3) the creativity in e-learning production, utilisation and administration. However, the process related to analysing, evaluating and predicting learners’ behaviour and outcomes in large-scale e-learning systems becomes a challenge. The course is an insight into this challenge..
During the course, students will be invited to get acquainted with the achievements of modern data analytics science. Students will get introduced with the basic theoretical concepts of data analytics. Students will participate in in-person talks, group work, and individual research and development. Also, students will be encouraged to perform e-learning data retrieval, analysis and visualisation problems..
To form knowledge about the big-data challenges in the domain of e-learning, students will practice in the retrieval, preparation, and analysis of data, and get familiarised with today's industry-leading languages and frameworks (e.g. JAVA, SCALA, KOTLIN, R and PYTHON). .
Also, students will review data harvesting from mobile devices, and also will get acquainted data retrieval in e-learning cloud environments..
The closing theme of the course contents is an overview of the problems of artificial intelligence and machine learning in e-studies..
The result of each module will be evaluated according to the student's capacity to engage in one of the four activity levels: (1) a report on the topic under consideration, (2) a thorough, in-depth study of the problem standing outside the course subject, (3) practical data processing that involves programming elements on the topic covered by the course, and (4) the useful approach of data analysis involving programming components, tools and languages for the theme expanding the course framework..
The student's achievements judged by the level and quality of his/her outcomes usage; by the degree of integration readiness in various e-learning infrastructures, disciplines, modules and training environments..
Goals and objectives
of the course in terms
of competences and skills
After completing the course students will be able: 1) to explain the idea and purpose of the basic theoretical concepts of data analytics; 2) to understand the challenges of data retrieval and preparation in the field of e-learning; 3) to prove skills to coordinate the retrieval, preparation and storage of e-learning data; 4) to plan the analysis of e-study data at the instructional level; 5) to operate with open-source and commercial programming frameworks, languages and tools used today; 6) to organise e-learning data retrieval and evaluation from mobile devices, cloud services for e-learning environments, artificial intelligence and machine learning.
Learning outcomes
and assessment
Students are able to discuss the theoretical concepts of data analytics science. - Assignment, assessed in on a 10-point scale
Students are able to argue on data retrieval and preparation challenges. - Assignment, assessed in on a 10-point scale
Students are able to organize e-learning data retrieve, gathering and cleansing. - Assignment, assessed in on a 10-point scale
Students are able to propose e-learning data analysis concepts at an organisational level. - Assignment, assessed in on a 10-point scale
Students are prepared to operate with modern open-source and commercial programming frameworks, languages and tools. - Assignment, assessed in on a 10-point scale
Students can retrieve and analyse learning-related data from mobile devices and cloud-based e-learning environments. - Assignment, assessed in on a 10-point scale
Students can classify and analyse the Artificial Intelligence and Machine Learning problems and challenges in e-learning environments. - Assignment, assessed in on a 10-point scale
Students are able to explain and discuss the aspects of e-learning technologies in a well-argued manner, both with specialists and other stakeholders. - Individual work, assessed as Pass/Fail
Students are able to independently develop their own and subordinate competences, take responsibility for their own and subordinate work, as well as plan and implement innovations in e-learning technologies related to the course topic. - Individual work, assessed as Pass/Fail
Course prerequisites Bachelor’s degree. Basic computer skills, skills working with cloud services and multimedia.

[Extended course information PDF]