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Algorithm development is the process of designing a step-by-step procedure or set of rules to solve a specific problem.
module-49, programming foundations, sample programK- Nearest Neighbor, popular as K-Nearest Neighbor (KNN), is an algorithm that helps to assess the properties of a new variable with the help of the properties of existing variables. KNN is applicable in classification as well as regression predictive problems.
analyse with SPSS, classification in supervised learning, Supervised learning, trend discoveryThis article is an empirical classroom implementation of an adjusted Bayesian Knowledge Tracing (BKT) method in a 120-hour Business Analytics training course delivered at GIET, Sonipat. The course used continuous quiz-based assessment alongside attendance, reporting, feasibility analysis, VIVA, and capstone evidence.
cognitive, knowledge tracing, module-58, personalisationAdaptive educational systems (AES) are intelligent systems that personalize learning experiences based on each student’s individual needs, behavior, and progress. These systems go beyond static content delivery.
cognitive, knowledge tracing, module-58This article develops a human-AI hybrid analytical thinking framework for strategic decisions under uncertainty. It argues that AI should not be treated either as a replacement for managerial judgment or as a passive analytical tool, but as one component of a governed decision workflow.
AI-Human Hybrid Thinking, business problem, business thinking, framework, module-61Estimating the difficulty of questions is a crucial step in designing quizzes or tests, as it directly impacts assessment validity, student motivation, and instructional feedback. In educational measurement, question difficulty refers to the proportion of students expected (or observed) to answer an item correctly. A balanced distribution of difficulty levels ensures that the assessment can […]
cognitive, module-58, personalisationThis article explores a robust, adaptive framework for incremental learning for sentiment analysis using the SGD Classifier.
coding, module-56, python-coding, sentiment analysis applicationFeature engineering is used after the data preparation step which involves handling missing values, removing duplicates, detecting outliers, and encoding categorical variables.
feature engineering, module-56, sentiment analysis application