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What are some common tools for learning analytics in higher education?

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Learning analytics is the use of data and analytics techniques to measure, understand, and improve learning and teaching in higher education. It can help institutions, educators, and learners to make informed decisions, enhance learning outcomes, and optimize resources. In this article, you will learn about some common tools for learning analytics in higher education and how they can benefit your learning experience.

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1 Data sources

One of the first steps in learning analytics is to collect and integrate data from various sources, such as learning management systems, student information systems, online platforms, surveys, assessments, and feedback. These data sources can provide rich and diverse information about learners' behaviors, interactions, preferences, performance, and satisfaction. However, collecting and integrating data also poses some challenges, such as data quality, privacy, ethics, and interoperability. Therefore, it is important to use appropriate data standards, formats, and protocols to ensure data validity, security, and compatibility.

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Nathalie DREAN ??????

?? Enseignement supérieur pluridisciplinaire , RH, Management, Communication, Digital, Projet Pro, Personnel Branding, Entrepreneuriat. Facilitatrice et top 1% LinkedIn ? Ecoles d'ingénieurs, de Management, Universités.
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Dans l'enseignement supérieur, l'utilisation d'outils courants d'analyse de l'apprentissage basés sur de nombreuses sources de données peut grandement améliorer l'efficacité de l'enseignement. Des méthodes telles que l'analyse des traces numériques permettent de suivre les interactions des apprenants avec les ressources en ligne. L'exploitation des données d'évaluation continue permet d'adapter le contenu pédagogique aux besoins spécifiques des étudiants. Enfin, l'intégration d'outils d'apprentissage automatique permet de détecter les éventuels problèmes d'apprentissage et d'apporter des solutions personnalisées.

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2 Data analysis

The next step in learning analytics is to analyze the data using various methods, such as descriptive, predictive, prescriptive, and explanatory analytics. These methods can help to answer different questions, such as what happened, what will happen, what should happen, and why it happened. For example, descriptive analytics can provide summaries and visualizations of the data, such as dashboards, reports, and charts. Predictive analytics can use statistical models and machine learning to forecast future outcomes, such as retention, completion, and grades. Prescriptive analytics can use optimization and simulation to recommend actions, such as interventions, feedback, and guidance. Explanatory analytics can use causal inference and theory to explain the underlying factors and mechanisms, such as motivation, engagement, and cognition.

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Lisdey E.

Head of Americas Higher Education and Insights Hub@ British Council | Politics and International Relations Expert PhD, FHEA
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In the dynamic landscape of learning analytics, the progression from descriptive to explanatory analytics forms a powerful continuum. Descriptive analytics lays the groundwork by offering a real-time snapshot of student engagement and performance. Predictive analytics leverages statistical models to forecast outcomes and anticipate challenges. Prescriptive analytics can recommend targeted interventions and personalisef feedback to foster student success. Finally, explanatory analytics delves into the "why," unraveling the underlying factors influencing motivation and cognition. Together, these methods create a holistic framework, providing educators with actionable insights for a data-driven approach to enhancing the learning experience.

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3 Data visualization

Another step in learning analytics is to visualize the data using various tools, such as graphs, maps, networks, and timelines. These tools can help to communicate and present the data in an effective and appealing way, such as highlighting patterns, trends, outliers, and relationships. Data visualization can also help to engage and empower the users of learning analytics, such as learners, educators, and administrators. For example, learners can use data visualization to monitor their progress, compare their performance, and identify their strengths and weaknesses. Educators can use data visualization to track their students' activities, assess their learning outcomes, and provide feedback and support. Administrators can use data visualization to evaluate their programs, allocate their resources, and improve their policies and practices.

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4 Data feedback

The final step in learning analytics is to provide feedback to the users based on the data analysis and visualization. Feedback can be formative or summative, individual or collective, immediate or delayed, and qualitative or quantitative. Feedback can also be delivered through various channels, such as emails, messages, notifications, alerts, and badges. Feedback can help to enhance the learning process, such as by increasing motivation, engagement, self-regulation, and metacognition. Feedback can also help to improve the learning outcomes, such as by facilitating learning gains, skills development, and knowledge transfer.

Learning analytics can offer many benefits for higher education, but it also requires careful planning, implementation, and evaluation. It is not a one-size-fits-all solution, but a context-dependent and user-centered approach. Therefore, it is important to consider the goals, needs, and preferences of the stakeholders, the characteristics and limitations of the data and tools, and the ethical and legal implications of the data use. By doing so, you can leverage the power of learning analytics to enhance your learning experience in higher education.

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Lisdey E.

Head of Americas Higher Education and Insights Hub@ British Council | Politics and International Relations Expert PhD, FHEA
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Data feedback in higher education provides timely insights into individual student performance, allowing educators to identify strengths and areas for improvement. This enables a more targeted and effective approach to teaching, fostering student growth. Additionally, data feedback plays a vital role in early intervention, allowing educators to identify students at risk of falling behind and implement support mechanisms promptly. Moreover, it promotes a culture of continuous improvement, enabling institutions to refine teaching strategies and curriculum based on data-driven insights. Ultimately, data feedback contributes to enhanced student engagement, improved learning outcomes, and a more adaptive and responsive educational environment.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Kimberly Bishop

Adjunct Associate Professor of Psychology and a Guest Expert Faculty Trainer at University of Maryland Global Campus and Psychology Professor at Southern New Hampshire University
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Student feedback and course reviews provide invaluable firsthand insights. As direct recipients of the educational experience, students have a unique vantage point from which to evaluate the efficacy of curriculum and instruction. Their perspectives get to the heart of our data collection efforts, for students are the raison d'être of the entire academic enterprise. By soliciting and analyzing their candid impressions, we gain crucial understanding that enables the continual refinement and enhancement of learning. Student voices illuminate the path forward.

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What are some common tools for learning analytics in higher education?

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1 Data sources

2 Data analysis

3 Data visualization

4 Data feedback

5 Here’s what else to consider

Higher Education

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What are some common tools for learning analytics in higher education?

1

2

3

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5

1 Data sources

2 Data analysis

3 Data visualization

4 Data feedback

5 Here’s what else to consider

Higher Education

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