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Learning Analytics

Learning Analytics is the process of sourcing, analysing and using data to gain insights and make decisions to improve student learning, performance and educational outcomes.

Sources

Current Trend

Use and Impact

Application

Tools

Implementation

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Learning analytics has the ability to profoundly change how organisations benefit from digital learning

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Click on the interactive images to discover more on how Learning Analytics is helping students, teachers and organisations in their digital learning experience

How does our organisation Implement LA? Nick: We used LA's at the basic level to analyse module and course completion rates amoungst students. I would be very keen to implement LA's in order to demonstrate ROI and help get leadership buy-in. George: My organisation is applying LA to judge the success of our e-learning on our Learning Management System (LMS). We are currently using the amount of course views to judge whether our learning has a great "pull" factor. We hope to build upon this by collecting data such as time spent on a course and pass/completion rates of quizzes etc to evaluate the learning we have on offer.

Real-life Implementation

Key Choking Points Data avalanche: Having too much data to analyse - Design to apply from the outset of the project. Define the who, why, what and when of the data you are going to collect and analyse. Privacy: Know your legal obligations Ethics: Be aware of bias, misuse of data and data sharing

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Adaptive Learning "Personalised and Predictive" Learning analytics help develop personalise learning experiences by analysing individual users learning data. It can help instructors identify at-risk students and provide targeted interventions before the problem exists. By analysing data on student progress (and capitalising on xAPI), instructors can identify areas where individual students may be struggling and adapt the learning experience with targeted interventions to better meet their needs.

Adaptive Learning

Business Impact of Learning Analytics In the business world, learning and development is typically thought of as a "cost center" however the learning analytics trend is being understood as a successful Return On Investment tool by using using key learner-centric Key Performance Indicator's (check out Kirk Patrick New World Model and Net Promoter Score). A general tendency to move from a "prove to improve" culture.

Business

Artificial Intelligence: With the arrival of ChatGPT late last year, the use of AI and machine learning to analyse large amounts of data and provide insights into student learning is certainly a big trend for 2023. AI can help identify patterns and trends that would be difficult for humans to identify, and can provide automated adaptive recommendations for students, instructors, designers and key leadership roles.

Artificial Intelligence

L&D Global Sentiment Survey 2023

L&D Global Sentiment Survey 2023

Best Practices Clearly Define Objectives: Learning analytics initiatives should be aligned with specific educational objectives, such as improving student engagement, reducing dropout rates, or increasing academic achievement. This requires clear definition of the metrics that will be used to measure progress towards these objectives. Use Multiple Data Sources: Learning analytics initiatives should draw on a range of data sources, including student behavior data, demographic data, and institutional data, to gain a comprehensive understanding of student performance and behavior. Involve Stakeholders: Learning analytics initiatives should involve a range of stakeholders, including educators, students, and administrators, to ensure that the insights gained from the data are meaningful and actionable. This requires clear communication about the goals of the initiative, the methods being used to analyze the data, and the implications for teaching and learning. Use Ethical Practices: Learning analytics initiatives should adhere to ethical principles, including data privacy, informed consent, and transparency. This requires clear communication with students about the data being collected and how it will be used, as well as measures to protect the security and confidentiality of the data. Focus on Actionable Insights: Learning analytics initiatives should focus on generating actionable insights that can be used to improve teaching and learning outcomes. This requires the development of clear and concise reports that communicate the insights gained from the data in a way that is easy to understand and act upon.

Best Practices

Linked Trends Artificial Intelligence: The trend of AI is enhancing the power and scope of learning analytics, enabling educators to gain deeper insights into student learning and provide more effective and personalized support. Personalization: One trend is the increasing use of learning analytics to personalize the learning experience for individual students. By analyzing data on student behavior and performance, instructors and educational technologies can provide targeted feedback and support to help students progress at their own pace and achieve their learning goals. Adaptive Learning: Another trend is the use of adaptive learning platforms that use learning analytics to provide students with a customized learning path that is tailored to their strengths and weaknesses. These platforms use machine learning algorithms to analyze student data and adjust the difficulty and content of learning activities to optimize learning outcomes. Gamification: Learning analytics can be used to analyze student engagement with gamified learning experiences, providing insights into how students interact with games and activities and identifying areas for improvement. This data can be used to optimize the design and effectiveness of gamified learning experiences. Competency-Based Learning: Learning analytics can be used to track student progress and mastery of specific competencies, providing insights into which students are ready to advance to the next level of learning. This data can be used to inform decisions about when to introduce new concepts, assign new activities, or provide additional support to struggling students. Microlearning: Finally, learning analytics can be used to analyze the effectiveness of microlearning experiences, which are short, focused learning activities that can be completed in just a few minutes. By analyzing data on student engagement and performance, educators can identify which microlearning experiences are most effective and refine their design to optimize learning outcomes.

Linked Trends

Ethical and Privacy Considerations Data Privacy: Learning analytics initiatives typically require the collection and analysis of large volumes of student data, including personal information such as student names, addresses, and grades. These data need to be securely stored and properly managed to prevent unauthorized access or misuse. Informed Consent: Students have the right to know what data is being collected, how it is being used, and who has access to it. Learning analytics initiatives should therefore obtain informed consent from students before collecting and analyzing their data. Bias and Discrimination: Learning analytics can be used to reinforce and amplify biases and discrimination that may exist in the educational system. For example, if the algorithm used to predict student success is based on historical data that reflects systemic biases, such as racial or gender biases, the algorithm may reinforce these biases rather than providing an accurate and fair assessment. Lack of Transparency: The algorithms and models used in learning analytics can be complex and difficult to understand, making it hard for students and educators to know how their data is being analyzed and how decisions are being made. Misuse of Data: Learning analytics data can be misused for purposes that are not aligned with the educational objectives of the initiative, such as advertising or profiling. There is a risk that data collected for educational purposes could be shared with third-party companies or used for other purposes without the student's consent.

Ethical and Privacy Considerations

Impact of Digital Learning Impact on Students: LA can help personalise the learning experience for students by tracking their progress, identifying areas where they are struggling, and suggesting resources to help them improve. Secondly, it can help students become more self-aware learners by giving them access to data about their own learning habits and performance. Finally, learning analytics can provide students with more immediate feedback on their progress, allowing them to make adjustments to their learning strategies as needed. Impact on Instructors: LA can help instructors identify struggling students early on, allowing them to intervene and provide targeted support. Secondly, it can help instructors identify effective teaching strategies and content by analyzing data on student engagement and performance. Finally, learning analytics can help instructors save time and resources by automating certain aspects of grading and feedback. Impact on Digital Designers: Digital designers can use learning analytics to improve the design of digital learning experiences. By analyzing data on student behavior, they can identify areas where students are struggling or disengaging and make adjustments to improve the learning experience. Learning analytics can also help designers track the effectiveness of different design elements, allowing them to make data-driven decisions about what works best. Impact on Managers: Learning analytics can be a powerful tool for managers in charge of digital learning programs. By analysing KPI's on student engagement and performance, they can identify areas where the program is succeeding or falling short and make data-driven decisions about how to improve it. Learning analytics can also help managers demonstrate the ROI of the program to stakeholders, such as funders or accrediting agencies. Finally, learning analytics can help managers allocate resources more effectively by identifying areas where additional support or interventions are needed.

Impact

Learning Analytics Tools Traditionally eLearning content was published using SCORM to record analytics. It has limitations as it cannot be used across multiple formats and platforms or in offline scenarios. Modern Learning Experiences should support the following two tools in order be future proof: 1. xAPI - (Experience Application Programming Software) It allows individuals to track all the learning activities across multiple formats and platforms and in offline scenarios. It is both human and machine readable, eg "John viewed Intro to Learning Analytics". The xAPI is sent, stored and retrieved from a LRS. 2. Learning Record Store (LRS): This is a storage centre for al xAPI content. It retrieves the information from multiple sources. The LRS forms an essential part of the LMS ecosystem. 3. Personalised Analytics Dashboard: Individualised for students, instructors and leadership roles.

Tools

Video Definition

Bilbliography https://donaldhtaylor.co.uk/insight/gss2023-results/ https://www.xenonstack.com/insights/what-is-learning-analytics https://steinhardt.nyu.edu/learning-analytics-10 https://www.learnupon.com/blog/calculate-elearning-roi https://elearningindustry.com/2023-ld-trends-learning-analytics https://www.watershedlrs.com/blog/learning-analytics/5-learning-analytics-trends-learning-technologies-2022/ https://link.springer.com/article/10.1007/s10639-022-11031-6 https://at.doit.wisc.edu/guides/what-are-the-pedagogical-uses-of-learning-analytics/ https://www.solaresearch.org/about/what-is-learning-analytics/ https://community.articulate.com/articles/introduction-to-the-tin-can-api-aka-xapi

Bilbliography

Pros Leadership Approval: L&D is typically thought of as a "cost center". Learning analytics can help prove ROI and encourage leadership buy in. Personalized Learning: Learning analytics can help instructors create personalized learning experiences for students by providing insights into individual strengths and weaknesses. This can lead to better engagement, higher satisfaction, and improved learning outcomes. Early Intervention: Learning analytics can help identify students who are struggling early on, enabling instructors to provide targeted support and interventions to help these students get back on track. Data-Driven Decision Making: Learning analytics provides instructors with data-driven insights into student performance, engagement, and behavior, allowing them to make informed decisions about teaching strategies, resource allocation, and course design. Improved Retention: By identifying at-risk students and providing them with additional support, learning analytics can help improve student retention rates and reduce dropout rates. Predictive Analytics: Learning analytics can use data to predict future student performance, enabling instructors to provide interventions and support before issues arise.

Pros

Cons Privacy Concerns: The use of learning analytics involves the collection and analysis of student data, which raises concerns around privacy and data security. Students and parents may feel uncomfortable with the level of data being collected and how it is being used. Potential for Bias: Learning analytics can introduce bias into the educational system, particularly when data is used to make decisions about students or teachers. This can lead to further inequities in education and reinforce existing biases. Overreliance on Data: Learning analytics can never be a replacement for human judgment and experience. Over-reliance on data can lead to a narrow focus on certain metrics and neglect of other important factors that affect student learning. Misinterpretation of Data: Learning analytics requires a deep understanding of data analysis and interpretation. Misinterpreting the data can lead to incorrect assumptions about student learning and can result in poor decisions. Incomplete Data: Learning analytics relies on data that may not capture the full picture of a student's performance or engagement. For example, a student's engagement in class may not be fully captured by online learning platforms or digital tools, and this can result in incomplete or inaccurate data.

Cons

What Data to Collect and Whats it's Use? DATA: What Sector/Department within an organisation the user is from? USE: Evaluate communication matrix within the Organisation. _______________________________________ DATA: User Feedback - Would you recommend this course to a colleague? (using Net Promoter Score)USE: Improve Learning Content_______________________________________ DATA: User activity within a community section (peer to peer learning).USE: This information could show which posts / comments sparks the most activity and engagement on the platform._______________________________________ DATA: Times of the day that the users use the platform the most.USE: For international organisations, you can schedule live meetings or customer support calls at the most widely frequented time._______________________________________ DATA: How much time a person spends on a distinct part of an e-learning. USE: We can infer that the longer someone spends on a task the more difficult they are finding it. This information could be used to identify problem areas or a lack of clarity in the learning. _______________________________________ DATA: What sections/lessons the customers have selected the most (hot spots of the page) on the platform.USE: Based on this information you can see what the customers are most interested in on the platform. Also if there are buttons that no one have pressed, this might mean that they are unclear (not seen as a button) or that it is not relevant to people. _______________________________________ DATA: What device a learner is accessing a course with? USE: One example, if they access from a mobile phone rather than a computer it could be assumed that the learner is often on the go. SOLUTION: Bitesized learning might be more effective for this person than a traditional e-learning course which requires a long time focusing. _______________________________________

What Data to Collect and Whats it's Use?

Course Marketing; How Learning Analytics helps? Students: Learning analytics can help encourage students to invest in education by providing personalized feedback on their progress and performance. This feedback can help them identify their strengths and weaknesses, focus on areas that need improvement, and stay motivated throughout their learning journey. Digital Learning Designers: Learning analytics can help digital learning designers invest in education by providing data on the effectiveness of their course designs. This data can be used to identify areas for improvement, optimize course content and delivery, and ensure that the learning experience meets the needs of learners. Leadership Roles: By using learner analytics to demonstrate the effectiveness of your course, you can win over the support of your leadership team. You can provide them with evidence of the course's impact on learners, its Return On Investment, and its contribution to the organisation's goals, increasing their buy-in and support.

Marketing