Learning Analytics and Knowledge 2013 Syllabus
Capturing and analyzing data has changed how decisions are made and resources are allocated in businesses, journalism, government, and military and intelligence fields. Through better use of data, leaders are able to plan and enact strategies with greater clarity and confidence. Data is a value point that drives increased organizational efficiency and a competitive advantage. Simply, analytics provide new insight and actionable intelligence. Companies such as Microsoft, IBM, Google, and Amazon are investing heavily in technologies and techniques in helping individuals and organizations makes sense of, and unlock the value within, big data.
In education, the use of data and analytics to improve learning is referred to as learning analytics. Analytics have not yet made the impact on education that they have made in other fields. That’s changing. Software companies, researchers, educators, and university leaders are starting to recognize the value of data in improving not only teaching and learning, but the entire education industry.
This course will provide an (generally non-technical) introduction to learning analytics and how they are being deployed in various contexts in the education field. Additionally, the tools and methods, ethics and privacy, and the systemic impact of analytics will be explored, presenting a broad overview of the current state and possible future directions of the field.
This course will be of interest to individuals across the full learning spectrum: K-12, higher education, corporate learning, and informal/life long learning. Leaders, educators, and even students will benefit from the topics explored and the related implementation issues (in particular, privacy and ethics of analytics).
At the conclusion of this course, participants will be able to:
1. Describe learning analytics and how it differs from related concepts such as educational datamining and academic analytics.
2. Analyze, plan, and deploy a small learning analytics pilot, including the intent of LA and tools needed to address analytics goals.
3. Develop a matrix of prominent learning analytics tools and the particular analytics strategies each tool addresses.
4. Evaluate current state of learning analytics technologies and describe the benefits and drawbacks to open source and proprietary tool sets.
5. Evaluate and describe the role of semantic web and linked data in next generation educational content.
6. Conduct basic analytics activities (such as importing and visualizing data) through in open source tools (R) and commercial tools (Tableau Software).
Various technologies will be used throughout this course. As the course progresses participants will share additional tools, so the tool set will increase.
Tools for taking the course:
Canvas Network (hub of the course)
Additional social media tools as selected and initiated by participants.
Tools for analysis:
Throughout LAK13, we will be experimenting with different tools. These tools will be used to demonstrate analytics activities that are relevant in different educational/training and development contexts. A sampling of tools that are available to learners include:
CMAP or VUE or Cohere
Open data sources: data.gov, Guardian data
Regional educational data
LAK/EDM data sets
Course syllabus is licensed: CC-BY-SA
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