October, 2019 At Unicon, a common challenge that we hear about is this: “We have lots of data everywhere, but we want a way to bring the data into one place so that we can analyze it and use it to ‘see’ how we are doing, possibly to spot trends and ultimately to inform decision making.”
May, 2019 Background and Overview In my previous article, AWS Lex Chatbot in the Classroom, I began exploring how to incorporate machine learning (ML) in the classroom. The myriad technologies and frameworks provided by Amazon Web Services (AWS) made it fairly simple to wire together processes and allowed me to focus on using the truly unique and valuable aspects of the chatbot to create a digital assistant that could provide definitions and a list of related terms in response to a student question.
April, 2019 Learning Tools Interoperability (LTI) is a standard developed by IMS Global Learning Consortium. It allows instructors and students to launch courseware and learning tools
March, 2019 Background and Overview Many current web applications provide some form of automated help and support for their customers. The value of this is two-fold. By providing an intuitive and high quality automated system, customers can self-serve many common questions without needing to wait for a person to provide the answers.
October, 2017 A new era in the story of Apereo uPortal is dawning: the era of uPortal 5. This new chapter brings many profound changes to uPortal. From my perspective, this new major version (version 5) is significantly more different from its precursor (version 4) than version 4 was from version 3. In fact, I would say this change (from 4 to 5) is more significant than any new major version since version 2.
October, 2017 As we explored earlier in this article series, "Separate Evolution of Two Systems" and "The Impact of Data on Student Success," disconnects between the two sides of campus computing, academic and administrative, can impact efforts to monitor and improve student success. And institutions today face mounting pressure to choose from a variety of solutions that help to mitigate or minimize this disconnect (see, "Possible Solutions"). Short of undertaking massive efforts to consolidate and centralize data systems for institutions, what is the best way to bridge the gap between existing systems in order to minimize negative impact?
September, 2017 In the first two articles of this series, "Separate Evolution of Two Systems" and "The Impact of Data on Student Success," we focused on the disconnect between the two sides of campus computing, academic and administrative, examining the impact of the separate evolution of these systems on student success. One of the most significant impacts of these compartmentalized systems is the difficulty to put data together to see a holistic picture of the student. Today, there is increasing awareness that a holistic view of student performance, based on accurate data, can positively impact student success.
June, 2017 This article is the second in the series, "Academic versus Administrative Computing: Bridging the Gap." Read the first article here: "Separate Evolution of Two Systems." If academic (LMS) and administrative (SIS) systems can coexist, each serving their own unique purpose, what's wrong with having different systems at the end of the day? There is certainly a good reason to keep a separation of concerns - why, for example, should learning systems have to worry about keeping track of a student's financial record or incoming test scores? Conversely, why should a back-end student information system (SIS) invest in keeping tabs on the student's learning experience?
March, 2017 Separate Evolution of Two Systems There's always been a disconnect between the two sides of campus computing: Academic and Administrative. Given the future needs of institutions, this gap will cause increasing difficulties as learning and records management become more intertwined.
June, 2016 Key Takeaways What tool is used to quantify organizational and technical areas of readiness? What can institutions learn from quantitative analysis of organizational and technical aspects of readiness? For many institutions, the idea of beginning a learning analytics initiative may seem overwhelming and complex. There is often the perception that too much work needs to be done before any type of initiative can be explored. You might be surprised to learn that most institutions are already prepared for some type of learning analytics initiative. Whether your institution is ready to use student data and visualize it on a dashboard, or even pursue a small-scale pilot, the quantitative and qualitative analysis of organizational and technical trends supports an overall sense of institutional readiness for learning analytics.