Learning analytics: Frequently asked questions and examples of use

Learning analytics aims to support the flow of studies. Fluency means that the student gets tools that can be used to plan, monitor and anticipate studies better and that support the student's self-regulation and well-being. Teaching is supported by learning analytics in that the teacher receives information about the course and its students, which helps the teacher plan and monitor the progress of the course and students, as well as develop teaching. Learning analytics aims to detect the need for support and guidance, so that the teacher and supervisor can offer the student support early, timely and equally. With learning analytics, the content and teaching of education programs are also developed, to meet e.g. as best as possible to the changing needs of working life. The university's management receives information about the completion of studies in aggregates, which help with the development of education and whose reporting is required, e.g. by law for granting national funding for education. 
 
Learning analytics produces useful information that would be difficult to achieve without it. At the same time, it should be remembered that learning analytics does not replace the student's, teacher's or supervisor's own interpretation of the situation. 

Analytics produces reports and visualizations so that the student can get comprehensive, up-to-date and proactive information about their studies. Learning analytics can provide the student with information about the flow of the courses, i.e. how the assignments are going and whether they are done on schedule, and more broadly about the study path, i.e. information about credit points, grades and the progress of the studies over time. 

Under the development work of learning analytics is that in the future information can be obtained about the workload of studies, when data from the personal study plan and course registrations are included in the tools. Up-to-date and future-oriented information is understood to influence more planned studying, which is connected to success in studies, the experience of workload and well-being. 

When the purpose of processing of personal data is based on University’s legal obligation or the public interest or the legitime interest, data may be used for LA to produce information to support organizing and developing teaching and supporting students to accomplish their studies. 

Data and information about students can only be used by those whose job description includes it.  

The data sources typically used by learning analytics are the study information system, learning environments and surveys, as well as feedback to the extent that their use has been agreed with the student. Regarding feedback, the typical practice is that they do not reveal the identity of the respondent and they are used more generally for the development of teaching and training programs. 

The teacher is allowed to collect and use the data accumulated from their own teaching to implement the teaching and to develop it. To help monitor and support the studies, the teachers see the students' information during the course (e.g. the completion of partial assignments and course activity and the course's preliminary information courses). Te student's advising and guidance team, i.e. the academic supervisor, and the study coordinator/planner of the degree program see the attainment information (credits and grades). In addition, the programme directors and the responsible professors of the majors have access to the student's performance data to help allocate monitoring support. 

When personal data (whether used as part of LA or not) is used as part of scientific research, the processing of it must comply with the conditions defined for such processing.   

Learning analytics can produce enriching information about students and the success of teaching, but they are only one source of information and their results should always be approached with criticism.  

Learning analytics produces information that is only as good as the data and processing tools it uses. The data and analysis methods selected for the use of learning analytics are carefully selected and tested before wider implementation. The basic information of the studies is correct, as long as it is entered correctly and up-to-date in the study register. The more diverse methods are used, such as recommendations and predictions, the more the accuracy of the results requires preparation and testing work before they are used in Aalto's common tools. The starting point for utilizing learning analytics is that students and teachers are presented as transparently as possible with the data it uses and the principles on which the results are based.  

Analytics data, methods and results should always be viewed critically. 

The aim of using learning analytics is that every student has an up-to-date and clear picture of how their studies are going. Accordingly, for the teacher, advisors and university management the aim is to provide comprehensive and timely information on how students are progressing at the course level and degree level, so that they can also be supported if necessary. It is important for the teacher and supervisor to get information about the students equally.  

Learning analytics can quickly and clearly provide the teacher with information about even large courses and their students, in which case the support is not so dependent on the information processing abilities or resources of the individual teacher. When utilizing analytics results for the targeting and timing of interventions (for the provision of support) it has to be ensured that no student can be left out of the scope of corresponding support or support services.  

Learning analytics, which produces information about the progress of whole study path, does not affect the evaluation. On the course level, the use of partial course study attainments or students’ study activity accumulated in learning environments is an already existing and typical teaching practice. The teacher can use this information to make a course grade in accordance with the goals and the criteria of the course. Learning analytics does not do course grade assessment independently, but the grade is formed based on the teacher's overall assessment. The course study attainments can be used also to produce various summary reports by leaning analytics, but at this point the actual course evaluation has already been done. The principle of good assessment is that its criteria are transparent to the student. 

Understanding support situations and their timing is important for teachers and advisors. For them,  comparing a student to others is not a self-worth. Already in current teaching and advising practices, the teacher and supervisor receive information about the student and the student groups to which the student compares, so that the implementation of the teaching and  guidance can be directed more appropriately. 

Learning analytics is not intended to be used for automatic decision-making regarding, e.g., the progress of studies, allocation of study places or recruitment. Learning analytics aims to create information for the planning and support of studies and teaching, which contribute to smooth progress. 

The goal is that with the help of learning analytics, information is obtained about the important stages of the thesis process where students need support. The digitization of the thesis process and its elements is progressing in such a way that in the future students and thesis supervisors will receive comprehensive and up-to-date information as well as recommendations for content and support. 

Students and the entire Aalto staff are told openly what kind of data is collected and what kind of information students, teachers and staff get to use. These are defined in the university's various documents, which include e.g. privacy notices, register statements of tools and services and learning analytics policy.  

If necessary, the student can make an information request about themself and a request for data rectification to the university. 

Regarding the use of personal data, the principles are defined in the general European regulation on personal data (General Data Protection Regulation, GDPR) and e.g. in the university's data protection statements and in the systems' register statements.  

The processing of data belonging to special categories of personal data groups is prohibited as learning analytics data. Such information includes race or ethnic origin, political opinion, religious or philosophical belief, trade union membership, health information, sexual orientation or behavior, and the use of genetic and biometric information to identify an individual. 

Data security issues are carefully checked and taken care of in accordance with existing regulations. This work is guided by information security guidelines and other information security documentation. These describe what kind of processes the educational institution uses to ensure information security and what is required of the students and staff themselves to ensure information security. 

Digital skills, which include the skills to read and interpret data and its results, have grown in importance. Basically, the student, teacher or other university staff does not need to know how to use learning analytics tools. In connection with the use of the tools, the aim is to offer them support for reviewing the results of reports and analytics. This is aimed at transparency and aims to arouse interest and skill in managing one's own digital footprint and reviewing them. 

The university has several reporting and analytics tools. Regarding individual systems, such as learning environments or reporting tools, their main users and other responsible persons can advise on the use. Their contact information can be found linked to that tool.  

The university's support services help with questions related to learning analytics 

Programme management services, Learning services (LES)  
specialist Jiri Lallimo, [email protected]  
specialist Amanda Sjöblom, [email protected]

With the help of analytics, the teacher gets comprehensive and up-to-date information about how the course is going, e.g. how individual students and the whole group participate, how the assignments go and whether they are done on schedule. The teacher can also use analytics to monitor the changes/impact of various pedagogical solutions within the course and between courses. 

No special permission is needed to use or create learning analytics.  

Learning analytics tools can be used by anyone whose work role has access to the data and/or results contained in the analytics. Aalto offers some ready-made tools for use by all students and staff, although the tools are just coming in more widely.  

The teacher can also do analytics themselves, i.e. use the data accumulated from their own teaching and customize analytics tools for himself (e.g. reports on the progress of the course and students), as long as the processing of personal data has been properly taken care of. 

It is possible to manipulate any metric, but in this case the performance under evaluation should be reconsidered. For example, if the focused object of the assessment is a page click or their amount, the student can manipulate the system / cheat the teacher, but such an object of review alone is insufficient as a basis for assessment.  

In general, it is more important to look at real outputs from a learning point of view, which should be set up for review through analytics. 

Automatic assessment can be used to evaluate the (partial) study attainments of the course. The teacher gives the final grade based on overall assessment. 

No. Automated decision making is not allowed. 

Decision-making is automated when decisions are based solely on the automated processing of personal data and the decisions produce legal effects concerning the data subject or significantly affect him or her. The processing in question includes profiling as defined in the GDPR insofar as it produces legal effects concerning the data subject or affects him or her in a correspondingly significant way. Automated decision-making and profiling | Data Protection Ombudsman’s Office (tietosuoja.fi) 

Depending on the analytics features available in  learning environment (e.g. MyCourses, A+) and the decision of the teacher how to implement them, the following analytics features may be available for the students 

1. the points from the assignments and possible feedback  

2. points accumulated for assignments (by assignment, weekly, entire course, etc.) and their visualizations (e.g. color coding of completed and undone assignments)  

3. the most recent visit and the number of visits to the learning environment or similar other information  

4. number of assignment returns used (A+)  

5. prediction of course grade (only in A+, not in MyCourses)  

6. offering personal assignments  

7. student feedback, course feedback and feedback feedback 

Depending on the analytics features available in learning environment (e.g. MyCourses, A+) and the decisions of the teacher how to implement them, the following analytics features may be available for the teacher 

1. Analytics related to students: 
   Participants in the course, scores and accumulation of points for partial assignments, number of refunds, login times, search functions (e.g. students who have reached or fall below a certain score), automatic and manual evaluation of partial assignments, collecting feedback from students and responding to it, identifying plagiarism, grouping students (e.g. basis), analysis of the interventions made by the teacher (interventions, i.e. support actions are in themselves independent events that analytics can provide information for identifying).
2. Analytics for the course and materials: 
   Comparison of course times from different years (at the annual, weekly, and assignment level), comparison of exams (at the annual course or assignment level), analysis of assignments and exercises, video viewing times, page downloads, collected feedback and its utilization in the development of materials, analysis of the teacher's interventions in the course.

Depending on the analytics features available in learning environment (e.g. MyCourses, A+) and the decisions of the teacher how to use them, the following analytics features may be available 

1. Automatic feedback and reports on exercises and their evaluation.  

2. Encouragement and positive feedback regarding exercises that are progressing well (cf. gamification and the research results obtained from it).  

3. Fro students who have fallen behind in the course (e.g. assignments for a certain week have not been completed) an email or other message is sent to them, reminding them of the assignments and possibly giving them additional time to complete them.  

4. Intervening in side effects. For example, if the student returns solutions to automatically evaluated exercises at very frequent intervals, or possibly also guidance on how to use time, if the student always solves tasks at night or very close to the deadline.  

5. Identifying known misconceptions and guiding the student based on that  

6. Examining students' returns with the aim of identifying plagiarism and pointing it out.  

7. Taking into account the students' pre-course knowledge (e.g. preliminary courses) and suggesting possible preparatory materials and assignments. 

1. Degree programme data report (requires user rights) 

2. Doctoral programme report (requires user rights) 

See also Programme director’s handbook  
“Using data of teaching and studying for programme development”