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Data structures and Algorithms (DSA) is a mandatory course for all discipline students to get placement in IT companies and to participate in competitive examinations including GATE and TANCET for their higher studies in a computer science discipline. DSA course focuses on how to organize, manage and store data in an efficient manner, which facilitates to access data easily and at a faster rate. Different types of data structures, its functionality and its applicability are discussed in this course. At the end of the course, students will have the capability to identify the suitable data structure for a problem. Due to its importance and complexity, pressure will be created on faculty members, who are handling this course. From the perspective of the student, some students understand the concept but lack knowledge of how to apply it. The majority of students struggle to comprehend the data structure and are perplexed by it. Recent work focuses on how faculty members play a major role in active learning like developing models to explain the concept, conducting activities like role play, think-pair share, flipped classrooms and so on. In this work, a study was conducted in the course DSA which focused on reflective practice led by David Kolb's experiential learning theory. An experiment was conducted during Academic Year 2021-22 (Odd) in the course 18CS340 -- Data Structures and Algorithms for a set of 54 students. It is inferred that the student gained a higher or deeper knowledge level in this course and is confident to identify appropriate data structures for real world problems. By engaging in reflective practice, faculty members can think around and reflect on their experiences, learn from them, make changes, and enhance their learning and instructional skills. [ABSTRACT FROM AUTHOR]

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today's on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings. [ABSTRACT FROM AUTHOR]

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner's ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner's behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user's interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner's objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner's performance and paves way for offering personalized learning experiences. [ABSTRACT FROM AUTHOR]