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.