The Efficiency Examination of Teaching of Different Normalization Methods

Normalization is an important database design method, in the course of the teaching of data modeling the understanding and applying of this method cause problems for students the most. For improving the efficiency of learning normalization we looked for alternative normalization methods and introduced them into education. We made a survey among engineer students how efficient could they execute the normalization with different methods. We executed statistical and data mining examinations to decide whether any of the methods resulted significantly better solutions

Automata Tutor v3

Computer science class enrollments have rapidly risen in the past decade. With current class sizes, standard approaches to grading and providing personalized feedback are no longer possible and new techniques become both feasible and necessary. In this paper, we present the third version of Automata Tutor, a tool for helping teachers and students in large courses on automata and formal languages. The second version of Automata Tutor supported automatic grading and feedback for finite-automata constructions and has already been used by thousands of users in dozens of countries. This new version of Automata Tutor supports automated grading and feedback generation for a greatly extended variety of new problems, including problems that ask students to create regular expressions, context-free grammars, pushdown automata and Turing machines corresponding to a given description, and problems about converting between equivalent models - e.g., from regular expressions to nondeterministic finite automata. Moreover, for several problems, this new version also enables teachers and students to automatically generate new problem instances. We also present the results of a survey run on a class of 950 students, which shows very positive results about the usability and usefulness of the tool

Using Lisp-based pseudocode to probe student understanding

We describe our use of Lisp to generate teaching aids for an Algo-rithms and Data Structures course taught as part of the undergrad-uate Computer Science curriculum. Specifically, we have made use of the ease of construction of domain-specific languages in Lisp to build an restricted language with programs capable of being pretty-printed as pseudocode, interpreted as abstract instructions, and treated as data in order to produce modified distractor versions. We examine student performance, report on student and educator reflection, and discuss practical aspects of delivering using this teaching tool

Experiment with Peer Instruction in Computer Science to Enhance Class Attendance

Class attendance of computer science courses in higher education is typically not overwhelming. Anecdotal reports and the authors’ experiences with a low-resource mode of peer instruction indicated increased class attendance after a lecture with such concept tests. This has been evaluated systematically with a 3rd-year computer science module using a medium-resource, software-based, Audience Response System (‘clickers’). Results show there is neither a positive nor a negative relation between lectures with peer instruction (PI) and class attendance. The student participation rate in software-based voting decreased and some decline in lecture attendance was observed. Thus, PI itself could not be shown to be a useful strategy to enhance class attendance. Notwithstanding, the students’ evaluation of the use of PI was a moderately positive

Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction

Reciprocal questioning is essential for effective teaching and learning, fostering active engagement and deeper understanding through collaborative interactions, especially in large classrooms. Can large language model (LLM), such as OpenAI's GPT (Generative Pre-trained Transformer) series, assist in this? This paper investigates a pedagogical approach of classroom flipping based on flipped interaction in LLMs. Flipped interaction involves using language models to prioritize generating questions instead of answers to prompts. We demonstrate how traditional classroom flipping techniques, including Peer Instruction and Just-in-Time Teaching (JiTT), can be enhanced through flipped interaction techniques, creating student-centric questions for hybrid teaching. In particular, we propose a workflow to integrate prompt engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a quiz-prompt-discuss routine to empower students to self-regulate their learning capacity and enable teachers to swiftly personalize training pathways. We develop an LLM-driven chatbot software that digitizes various elements of classroom flipping and facilitates the assessment of students using these routines to deliver peer-generated questions. We have applied our LLM-driven chatbot software for teaching both undergraduate and graduate students from 2020 to 2022, effectively useful for bridging the gap between teachers and students in remote teaching during the COVID-19 pandemic years. In particular, LLM-driven classroom flipping can be particularly beneficial in large class settings to optimize teaching pace and enable engaging classroom experiences.Comment: Submitte

MOOCs: Expectations and Reality

This comprehensive study of MOOCs from the perspective of institutions of higher education includes an investigation of definitions and characteristics of MOOCs, their origins, institutional goals for developing and delivering MOOCs, how MOOC data is being used, a review of MOOC resource requirements and costs, and a compilation of ideas from 83 interviewees about MOOCs and the future of higher education. We identify six major goals for MOOC initiatives and assess the evidence regarding whether these goals are being met, or are likely to be in the future

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Consumo energético de métodos iterativos para sistemas dispersos en procesadores gráficos

La resolución de sistemas de ecuaciones lineales dispersos de gran dimensión es una de las operaciones más comunes en aplicaciones científicas y de ingeniería. El aumento de sus tamaños propicia el desarrollo de técnicas de Green Computing, que permiten diseñar aplicaciones conscientes de la energía, en las que la eficiencia energética es el objetivo prioritario. En este Tesis Doctoral se ha diseñado una metodología basada en “técnicas de fusionado de kernels CUDA” que reduce el número de kernels, y con ello, costes de lanzamiento y transferencias de información. Su uso, junto con la sincronización de las GPUs en modo blocking, permite reducir el consumo energético en sistemas de cómputo heterogéneo, CPU-GPU. Estas técnicas tienen especial interés en GPUs que soporten paralelismo dinámico. La aplicación de esta metodología en la resolución de sistemas de ecuaciones lineales dispersos muestra mejoras destacables en eficiencia energética, obteniendo un compromiso entre rendimiento y consumo energético