Jisc has been supporting seven research projects in learning analytics at UK universities over the past year. These have been in the areas of curriculum analytics, mental health and wellbeing and the evaluation of institutional learning analytics projects.Join us to hear the projects present their interesting findings.
There’s no question that the shift to remote and flexible learning has highlighted the importance of technology in education, but at the same time, this shift has also complicated some key aspects of a teacher’s job.
The Experience API (xAPI for short) is far more than just an update to SCORM, the popular standard for tracking data from a learning management system. xAPI opens up a whole new world of possibilities for learning analytics. Examples of what real organizations are doing with it in real-life situations make it easier to grasp the scale of this advance and apply the learnings to your own situation.
According to The Kirkpatrick Model, Level 3: Behavior is the degree to which participants apply what they learned during training when they are back on the job. The prevailing belief that a Level 3 plan for post-training support and accountability is difficult, expensive and out of training’s purview is untrue. Here are the deceptively simple steps to create learning experiences with true value.
The OLC Quality Scorecard - Benchmarking Tools, Checklists, & Rubrics for Evaluating the Quality and Effectiveness of Online Learning Programs & Courses
How can product developers use data analytics to improve products, prove their effectiveness, and increase the fidelity of implementation? Learn more in the latest Nexus story by Rachel Schechter
Natercia Valle tells a cautionary tale about the use of learning analytics dashboards to increase student motivation, and the challenges of translating theory into design solutions.
March 20, 2022
Artificial intelligence in higher education isn't without its risks. Here are three possible trouble spots for the use of AI. Elana Zeide is Associate Professor of Law at the University of Nebraska.
In dieser Fortsetzungsfolge zum Thema Learning Analytics erläutern Marius Wehner und Lynn Schmodde von der Wirtschaftswissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf das Verbundprojekt Fair Enough. Zur Fairness von Learning Analytics-Systemen legen sie empirische Evaluationsergebnisse verschiedener Stakeholder-Gruppen dar und geben einen Ausblick auf zukünftige Entwicklungen. Interviewer in Folge 12 des DINItus Podcasts ist Erik Reidt vom ZIM/Multimediazentrum der HHU Düsseldorf.
Sunday Blake dives into the latest in learning analytics and engagement data, and asks how universities can act upon it to make our interactions with students more human.
The Experience API (xAPI) allows us to collect data about any type of learning experience or activity, but does that mean we should? Should we generate massive amounts of xAPI data for every possible type of interaction and then expect to make sense of it all later? This approach can be costly in terms of data storage, but also in terms of your time.
This is a sequel test after the chemical analysis and microbiological procedures have been conducted. The study determined the level of acceptability of the by-product of Talisay (Terminalia catappa) nuts specifically; Talisay Nuts Polvoron, Glazed Talisay Nuts, and Sugar-coated Talisay Nuts using sensory evaluation as to appearance, taste, aroma, sweetness, and texture. The responses of the food inclined participants are described yielding from the Hedonic Tests conducted and statistically treated. Results concluded that the developed products are remarkably acceptable and marketable.
These measurements are indispensable for tracking the results of your chatbot, identifying any stumbling blocks and continuously improving its performance. But which metrics should you choose?
The ultimate guide to chatbot analytics. Find out what bot metrics and KPIs you should measure and discover easy ways to optimize your chatbot performance.
We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. The code and weights, along with an online demo, are publicly available for non-commercial use.
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