“Learning analytics refers to the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues. Data are collected from explicit student actions, such as completing assignments and taking exams, and from tacit actions, including online social interactions, extracurricular activities, posts on discussion forums, and other activities that are not directly assessed as part of the student’s educational progress. The goal of learning analytics is to enable teachers and schools to tailor educational opportunities to each student’s level of need and ability in close-to-real time. Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses and aims to leverage the vast amount of data produced by students in academic activities.” Horizon Report, 2012
Following some more definitions about Learning Analytics (LA)
“Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning.” Georg Siemens (2010)
“Learning Analytics is the interpretation from specific learner data with the goal to purposefully improve the individual learning process.” Martin Ebner & Martin Schoen (2012)
Watching the video from the online event (you can find it in the OPCO12 course blog or on Jochen Robes Weiterbildungsblog) and reading about Learning Analytics in the Horizon generated a lot of question. The chat protocol and the blog posts about this topic so far already indicate that Learning analytics is a controversy topic that evokes quite a lot of emotions.
Here are some questions that came to my mind during reading, which are pretty much in accord with the questions and concerns raised in the chat and blog posts.
- Actually the first thought that crossed my mind already after reading the abstract was ‘Big Brother is watching you’ similar to Trilian who wrote in his blog ‘isn’t it virtual brother who is logging us?’
- Who will collect (data mining) the data from which sources?
- Who will analyze and interpret the data and which standards/criterion will be applied for analysis?
- Will students receive information what type of data will be collected and will they be able to access the data?
1. ‘Big Brother theory’
Sonja Gerber raised the question in her blog if the transparent students develop (‘gläserner Student) and Martin Ebner from the Technical University in Graz cited in his presentation during the online session Erica Naone that ‘Anonymous was yesterday’.
Being an online student for more than six years at the OU, I know that my digital footprints are everywhere in the Web. At first I was somehow worried that which each progressing study year a Google search brought up more hits on my name, but I acknowledge Will Richardson (2008) statement “It’s a consequence of the new Web 2.0 world that these digital footprints—the online portfolios of who we are, what we do, and by association, what we know—are becoming increasingly woven into the fabric of almost every aspect of our lives.” (Richardson, W. (2008) ‘Footprints in the Digital Age’)
In the field of e-learning an increasing number of academics establish their academic identity by maintaining academic blogs, consequently pushing their reputation and credibility.
Especially during my Master study (Online and distance education – MAODE) all kind of data was collected i.e. posts on discussion forums, wiki contributions, completing assignments and probably other data I was not aware of. Whether the OU and/or tutors made really use of it and used it to analyze our learning and tailor educational opportunities to each student’s level of need and ability in close-to-real time, I don’t know for sure. The questionnaires about teaching quality, course design and the quality of learning material we were asked to complete during the course did definitely not result in close-to-real-time adjustments. The sense and purpose of these surveys where pretty much questioned, because if you cannot or don’t want to change anything during an ongoing course why ask for feedback?
2. Data mining and data security/privacy (4)
Jochen Robes raised in his presentation among others the following questions: What are ‘learner data’ and to whom the data belongs?
Learner data is learner-produced data (Siemens), traces that learners leave behind (Duval) and/or data collected from explicit student actions, including online social interactions, extracurricular activities, posts on discussion forums, and other activities that are not directly assessed as part of the student’s educational progress. Yet, the Horizon Report claim that LA ‘must include more than LMS data’, but which data?
What constitute learner data is broadly defined and the sources from which data can and should be collected are as well not clearly described. Data from online social interactions and other activities describes a broad range. Robes question to whom the data belongs coincide with question 4 whether students will receive information about what type of data will be collected and whether they will able to access the data? Data security was one main topic raised in the chat and I think it requires particular considerations when discussing LA.
3. Who will analyze and interpret the data and which standards/criterion will be applied for analysis?
This question was also raised in the chat. Collecting and analyzing data is very time-consuming. Ebner claims that this is not reasonable for teachers, but that means that teachers have to rely on the analysis from somebody else. Martin Ebner introduced a traffic light system. Green traffic light means that the student is good to go and ‘red’ that the student performs bad and is about to fail. But who sets the standards/criterion for red, yellow and green?
Nevertheless, despite the many questions and concerns the relevance for teaching and learning should not be underestimated. If handled deliberate and considerate Learning analytics could indeed help to improve understandings of teaching and learning, and to tailor education to individual students more effectively. (Horizon Report, 2012), predict and advise on learning (Siemens) and improve the individual learning process. (Martin Ebner & Martin Schoen).