Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowledge. To streamline this process for higher-education courses, we employed GPT-4 to generate KCs for multiple-choice questions (MCQs) in Chemistry and E-Learning. We analyzed discrepancies between the KCs generated by the Large Language Model (LLM) and those made by humans through evaluation from three domain experts in each subject area. This evaluation aimed to determine whether, in instances of non-matching KCs, evaluators showed a preference for the LLM-generated KCs over their human-created counterparts. We also developed an ontology induction algorithm to cluster questions that assess similar KCs based on their content. Our most effective LLM strategy accurately matched KCs for 56% of Chemistry and 35% of E-Learning MCQs, with even higher success when considering the top five KC suggestions. Human evaluators favored LLM-generated KCs, choosing them over human-assigned ones approximately two-thirds of the time, a preference that was statistically significant across both domains. Our clustering algorithm successfully grouped questions by their underlying KCs without needing explicit labels or contextual information. This research advances the automation of KC generation and classification for assessment items, alleviating the need for student data or predefined KC labels.

Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, researchers can search for the optimal individualized treatment rule (ITR) that maximizes the expected outcome. Existing methods typically require initial estimation of some nuisance models. The double robustness property that can protect from misspecification of either the treatment-free effect or the propensity score has been widely advocated. However, when model misspecification exists, a doubly robust estimate can be consistent but may suffer from downgraded efficiency. Other than potential misspecified nuisance models, most existing methods do not account for the potential problem when the variance of outcome is heterogeneous among covariates and treatment. We observe that such heteroscedasticity can greatly affect the estimation efficiency of the optimal ITR. In this paper, we demonstrate that the consequences of misspecified treatment-free effect and heteroscedasticity can be unified as a covariate-treatment dependent variance of residuals. To improve efficiency of the estimated ITR, we propose an Efficient Learning (E-Learning) framework for finding an optimal ITR in the multi-armed treatment setting. We show that the proposed E-Learning is optimal among a regular class of semiparametric estimates that can allow treatment-free effect misspecification. In our simulation study, E-Learning demonstrates its effectiveness if one of or both misspecified treatment-free effect and heteroscedasticity exist. Our analysis of a Type 2 Diabetes Mellitus (T2DM) observational study also suggests the improved efficiency of E-Learning.

With the global pandemic in place, almost every college and university has moved towards e-learning platforms. With the introduction of the learning management system in different parts of the world, it has become easier for schools, colleges, and universities to reach out to students. E-learning has had its share of success. Stats show that the retention rate for students taking classes is more when compared to traditional classroom learning. The learning management system has proved to be an added advantage.

Advances in AI, making use of machine learning (ML) and natural language processing (NLP) have proven adept for voice recognition and identifying …

Distance learning and remote teaching have increased reliance on tech making it a reality, and able to traverse borders with less regard for physical geo-locations. There are numerous restrictions that prevent online learning from being ubiquitous such as internet accessibility, access to learning platforms, adequate attention for learners individually, and language barriers. Video-based learning could be enough for urban pupils, but for rural areas, connectivity becomes low, less reliable, and interrupted lessons. For international students, pursuing higher education or probably taking vocational courses, a lack in fluency in English or any other intermediary languages can play a significant role in limiting proper online learning. Learning a new language is the objective for work or to further studies, but the bigger question is how technology can bridge the language learning divide.

We can't hang out somewhere nice, it's been so long since we last met our friends or families who are stuck overseas and we can't hope for a major change or revolution because the future is that uncertain and this uncertainty just grows every day. It's miserable and sad to see the current state people are in, but kudos to the people who are fighting with their or their loved ones' lives every day, struggling in these trying times, and to the front liners in the health sector who are trying their best to put every possible effort to provide and save the lives of the masses.

Online learning (aka E-Learning) is now considered to be an integral part of the education sector. In simple words, online learning refers to the type of learning where the learning process is mediated by the internet i.e. the learners use the internet to learn. Online learning is gaining tremendous popularity. It is also said to increase the knowledge retention rates from 25-60% in comparison to face-to-face training. Online learning owes much of its popularity and efficiency to machine learning (ML) and artificial intelligence (AI).

From social media to speech recognition, warfare to writing articles, coding to customer service – machine learning and artificial intelligence have become part and parcel of our urban lives. In the pursuit to make life smoother and more comfortable, man has tapped into the potential of AI and invented auto-driven cars, smart sensors to capture spectacular photos, and home assistant devices. Similarly, AI is taking the field of education by storm and replacing traditional methods by the minute. Thanks to AI, the academic world has become more personalized, thus changing the way of e-learning. People can now access educational materials with just a click on their phones and laptop.

For many years, e-learning designers have relied on chatbots -- those handy little digital "helpers" that pop up to answer questions or provide additional information. With the introduction of faster internet connections (5G and beyond) and better development tools, chatbots are no longer just digitized voices behind animated avatars. Chatbots are rules-driven services, sometimes powered by artificial intelligence (AI), that help individuals communicate in an online environment. In an e-learning context, AI-powered chatbots make learning more intuitive by helping learners choose, consume and understand content. Rather than clicking buttons, selecting drop-down menu items or tapping on a screen, with chatbots, learners can navigate content through gestures and conversational interactions.

In recent years, there has been a lot of focus on adaptive e-learning, fueled by the advances of machine learning and artificial intelligence. As the one-size-fits-all approach of e-learning loses its appeal and online course attrition rates continue to rise, there is a move toward more personalized and adaptive learning to engage learners and achieve better learning outcomes. Personalized and adaptive learning has the ability to change learning content or the mode of delivery on the fly and to provide real-time feedback to learners. The origin of adaptive learning came from the research of intelligent tutoring systems, recommender systems and adaptive hypermedia. The advent of machine learning and artificial intelligence techniques have helped the plethora of platforms and tools that support adaptive learning flourish.