Data Driven Classroom Interviews (DDCIs) are an interviewing technique that is facilitated by recent technological developments in the learning analytics community. DDCIs are short, targeted interviews that allow researchers to contextualize students' interactions with a digital learning environment (e.g., intelligent tutoring systems or educational games) while minimizing the amount of time that the researcher interrupts that learning experience, and focusing researcher time on the events they most want to focus on DDCIs are facilitated by a research tool called the Quick Red Fox (QRF)--an open-source server-client Android app that optimizes researcher time by directing interviewers to users that have just displayed an interesting behavior (previously defined by the research team). QRF integrates with existing student modeling technologies (e.g., behavior-sensing, affect-sensing, detection of self-regulated learning) to alert researchers to key moments in a learner's experience. This manual documents the tech while providing training on the processes involved in developing triggers and interview techniques; it also suggests methods of analyses.

A new phenomenon has recently emerged that has made it difficult for people to find'the one,' a study has revealed. Researchers found young adults are suffering from'social media confusion' caused by the platforms as well as dating apps. The sites increase the temptation and desire for a new partner, making people less likely to stick it out in a relationship, the researchers say. And users are exposed to more attractive and wealthy people than ever before, which is distorting their expectations in a potential mate. The team suggested that people ages 18 to 30 are now valuing'pleasure' over long-term stability.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Pepperdata, a provider of tools that optimize IT infrastructure for computation, has seen a lot of trends come and go over the years. Now organizations are using the company's tools to optimize infrastructure to process AI models. VentureBeat caught up with Pepperdata CEO Ash Munshi to gain a deeper appreciation for IT issues, such as data management, that are holding back the rate at which enterprise IT departments can meaningfully implement AI. But he also pointed out that a lot of companies struggling with AI might be fighting the wrong battle for their business needs.

Deep learning, or a class of machine learning algorithms, is showing great promise, primarily because it's getting results. Deep learning historically was largely inaccessible because it had such high demand on computational resource and data, but with the progression of technology, storage costs have come down and the computation has gone up, said CEO of Bonsai, Mark Hammond. It demands a lot of resources, but there are a few organizations focusing on making this technology and research accessible and easy to use (think: OpenAI or Google's DeepMind). According to vice president and principal analyst at Forrester Research, Mike Gualtieri, machine learning is trying to find a predictive model, whereas deep learning is based upon a hierarchical network, roughly fashioned after the human brain, he said. Since it's modeled after the brain, people argue that deep learning actually lets systems "learn," said Gualtieri.