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Breakthrough Innovation in Education: EdTech Startups and their Learning Analytics Technologies

Learning analytics is an emerging subject discussed by many authors in the literature. By utilizing computational tools and statistical models on student data underlying patterns can be identified to derive knowledge. In addition, learning analytics provides a positive impact on learning engagement, personalization and supporting students emotional mindset. Many universities already identified the benefits of learning analytics and therefore developed tools individually. Nonetheless, startups also identified the potential of learning analytics and started to create their own products. This thesis discusses three startups (Knewton Alta, Acrobatiq, Civitas Learning) that focus on learning analytics in higher education and examines their provided analytic tools. In particular, the thesis evaluates their proposed solutions to tackle challenges mentioned in the literature. In addition, a learning analytic tool (Bookshelf CoachMe) is examined and evaluated in regards to their degree of improving learning engagement and personalisation.

📋 Key facts

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Research question 1: Analyze the current technologies applied by learning analytics startups. What methods are used to increase learning engagement / personalization? What challenges are mentioned in the literature and how do the startups cope with these challenges?

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Research question 2: Assess a learning analytic tool in regards to the degree of learning engagement and personalization.

👩🏼‍💻 Review of Learning Analytics Startups

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Knewton Alta and Civitas Learning belong to the first companies founded, which focus on learning analytics.

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Acrobatiq is another startup which was aquired by Vitalsource.

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Challenges

💡 Results of research question 1:

Predictive algorithms reduce students course failure rate and increase student persistence
Startups focus on increasing student engagement and personalization by providing tasks adapting to students knowledge gaps
Visual dashboards show further statistical insights to help tutors adjust courses and approach students optimaly
Ethical impact and emotional support are not covered by the startups

👩🏼‍💻 Case Study Bookshelf CoachMe by Vitalsource

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Within the thesis the learning analytic tool provided by Vitalsource named Bookshelf CoachMe was assessed directly by the author. The learning analytic feature can be considered as an add-on to digital books, which automatically generates questions related to the learning content. Answering the questions helps the user to check their own level of knowledge and to deepen the learning objectives. In order to be able to assess the feature, a case study is conducted measuring the level of learning engagement and personalization. In particular, learning improvement, engagement and personalization in learning are investigated on the one hand with the textbook itself and on the other hand with the the learning analytics tool Bookshelf CoachMe.

🖼️ Thesis Infographic

📈 Outlook

The results of the use case carried out supports the statement, that learning analytics improves learning and teaching. However, it should be noted that the tool Bookshelf CoachMe was only evaluated with n=1 participants and on one textbook/digital book. In the future, the results should be evaluated with a larger number of participants as well as a variety of books to obtain statistical significance and to reduce bias.
After reviewing existing learning analytics startups, one can conclude that the companies provide emotional support only sparsely. Therefore, startups should evaluate emotionally supporting students by employing questionnaires or deep-learning methods based of video material to be more aware of students emotions.
One challenge that has been recognized in the literature concerns the ethical compliance of learning analytics methods. In the future the tools should by evaluated in regards to the ethical background. In the future, tools should be evaluated in terms of ethical background.
For the purposes of this work, the evaluation focused mainly on assessing learning analytics tools in regards to their level of engagement and personalization. Several authors mentioned that the use of certain learning approaches coupled with analytics methods improves learning speed by up to six times. In the future, the actual learning improvement in regards to the learning speed should be examined.