Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovative application of Language model-based Knowledge Tracing (LKT) to programming education. CodeLKT leverages pre-trained language models to process learning data, demonstrating superior performance over existing KT and Code KT models. We explore Domain Adaptive Pre-Training (DAPT) and Task Adaptive Pre-Training (TAPT), showing enhanced performance in the coding domain and investigating cross-domain transfer between mathematics and coding. Additionally, we present an theoretically-informed integrated system combining CodeLKT with large language models to generate personalized, in-depth feedback to support students' programming learning. This work advances the field of Code Knowledge Tracing by expanding the knowledge base with language model-based approach and offering practical implications for programming education through data-informed feedback.

EDITOR'S NOTE: This is the first of Automotive News Canada's two-part look into artificial intelligence in the Canadian auto industry. Scanning an employee badge at a Martinrea International Inc. plant is no longer reserved for the front gates. Today, operators swipe into assembly equipment with their keycards, logging details about how well they have been trained, their experience on the machines and what output level they can achieve. Sifting through the evolving stream of data using artificial intelligence (AI) helps match the right operator to the right machine, said Ganesh Iyer, chief technology officer at the Toronto-based supplier. The process is one in a growing arsenal of AI tools aimed at improving speed and precision at automakers and parts suppliers.

Sifting through the evolving stream of data using artificial intelligence (AI) helps match the right operator to the right machine, said Ganesh Iyer, chief …

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Join this VB Live webinar to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results. Most business leads are aware of the importance of AI, says Michael Butler, head of customer success at Ople, but often don't know how to get started – or if an investment in AI technology is the smartest route to stacking up real ROI. Previously, as director of global ecommerce at VMWare, he was relying entirely on his data science team, Butler says. The team consisted of about 35 people on staff full time, and the problem was that they were slow to produce models and results. For instance, coming up with a model to score customers most likely to buy a new release would take six weeks; when an anniversary sale came along, it would take another six weeks, starting from scratch each time. It should take a matter of days, if not hours, Ople thought.

Every day seems to bring a new outrageous headline with promises of artificial intelligence's superpowers. The hype is very high right now, with headlines conveying the sense that disruption is imminent and everything will soon be overtaken by these technologies. The reality, however, is very different. Yes, artificial intelligence (AI) does have a lot of potential and it's an exciting field. However, not every organization has the right foundation in place to deliver real results.

Artificial intelligence--a broad set of technologies that enable machines to mimic the human brain's ability to process information, learn and adapt--holds potential in healthcare to improve patient outcomes and reduce costs, but it hasn't yet been widely adopted in daily clinical practice. However, some leading healthcare organizations, such as the Cleveland Clinic, Intermountain Healthcare and others, are beginning to build the infrastructure and data science capabilities to use AI to deliver clinical and financial benefits. While some industries are using AI programs designed to recognize speech, written language or visual data or do problem-solving, health systems are gaining experience with machine learning, a subset of AI focused on finding patterns or relationships in data in an iterative, or learning, fashion. Early projects have demonstrated promising results. In some of these cases, healthcare organizations have purchased a commercial tool to help them reach a specific clinical goal, such as reducing hospital readmission rates or predicting which patients are at highest risk of becoming expensive cases.

Data-driven digital marketing is big business. According to eMarketer some 55% of all digital advertising dollars will be driven by programmatic initiatives in 2015 where computer speed and machine learning take precedence over human guess work. By 2016 that number is expected to rise to 63% representing over $20 billion in programmatic ad buys. Legions of data scientists and math Ph.Ds have taken over the digital advertising business. They are serving to enhance efficiency for the notoriously inefficient business of marketing.