Title: T1-A3 Osnove analitike učenja
Course: Learning Analytics

Feedback shared by this category's questions, if right: Vaš odgovor je točan.
Feedback shared by this category's questions, if partly right: Vaš odgovor je djelomično točan.
Feedback shared by this category's questions, if wrong: Vaš odgovor nije točan.

Q: What is the primary goal of learning analytics?
- More efficient administration of an educational institution.
- Automation of student assessment.
- Improvement of learning and teaching processes.
- Generation of financial reports.
A: Improvement of learning and teaching processes.

Q: Which type of learning analytics analyzes existing data in order to predict future outcomes?
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
A: Predictive

Q: Match the types of learning analytics with their descriptions.
- Descriptive | Shows what happened
- Diagnostic | Explains why something happened
- Predicitive | Forecasts what will happen
- Prescriptive | Suggests what to do

Q: What is the main difference between learning analytics (LA) and educational data mining (EDM)?
- LA uses exclusively statistical methods, while EDM uses artificial intelligence.
- LA focuses on improving learning, while EDM is more oriented toward technological and research goals.
- EDM is used only in primary schools, while LA is used in higher education.
- EDM is a new branch of LA developed in 2022.
A: LA focuses on improving learning, while EDM is more oriented toward technological and research goals.

Q: What are the challenges of successfully implementing learning analytics in educational institutions according to the literature? (Multiple answers are possible.)
- Lack of institutional strategies and policies.
- Inconsistent stakeholder involvement.
- Limited trust in learning analytics.
- Excessive consideration of students’ opinions.
- Varying quality of input data.
- Excessive interest from teachers.
A: Lack of institutional strategies and policies.; Inconsistent stakeholder involvement.; Limited trust in learning analytics.; Varying quality of input data.

Q: Which statement best describes the contribution of teachers in the context of data-driven decision-making?
- Teachers passively observe the data provided by institutions, without the possibility of influencing any decisions.
- Teachers use data to evaluate their own practice, identify learning challenges, and adapt activities to students.
- Teachers analyze data exclusively for the purpose of producing reports for accreditation procedures.
- Teachers manage learning analytics systems and develop algorithms for predictive models.
A: Teachers use data to evaluate their own practice, identify learning challenges, and adapt activities to students.

Q: What risks arise in the context of predictive learning analytics? (Multiple answers are possible.)
- Bias in models based on incomplete and biased input data.
- Unjustified labeling of students.
- Insufficient understanding and incorrect interpretation of results among teachers.
- Reduction in the need for teachers’ pedagogical decision-making.
- Risk of privacy violations if data are not properly protected.
A: Bias in models based on incomplete and biased input data.; Unjustified labeling of students.; Insufficient understanding and incorrect interpretation of results among teachers.; Risk of privacy violations if data are not properly protected.