1,721,108 research outputs found
Abstract specification of structures and methods in symbolic mathematical computation
No full textAbstractThis paper describes a methodology based on the object-oriented programming paradigm, to support the design and implementation of a symbolic computation system. The requirements of the system are related to the specification and treatment of mathematical structures. This treatment is considered from both the numerical and the symbolic points of view. The resulting programming system should be able to support the formal definition of mathematical data structures and methods at their highest level of abstraction, to perform computations on instances created from such definitions, and to handle abstract data structures through the manipulation of their logical properties. Particular consideration is given to the correctness aspects. Some examples of convenient application of the proposed design methodology are presented
K-OpenAnswer: a simulation environment to analyze the dynamics of massive open online courses in smart cities
No full textThe smartness of a city is given by the technologies it put to use, and more than that, by the people empowered by such technologies; it is worth thinking about how people can be trained to be empowered by smart technologies, and how cities can become “educational.” So, while sustainability and technology solutions for smart cities are strategic challenges, one of these is surely distance education and training. In this field, the Web offers many opportunities, such as the e-learning platforms where students can learn, according to their own needs and pace. The massive open online courses (MOOCs) are particular distance learning platforms, generally offering, so far, free courses on a huge amount of topics, and characterized by a (potentially) very high number of enrollments. In a MOOC, a teacher, or tutor, has a hard life when trying to follow and manage with the learning processes of thousands of students. In particular, assessment can be managed almost exclusively by letting the student answer questions in closed answers tests. This strategy has some didactic limits, while a valid alternative is to use peer assessment (PA) over more articulated assessment activities (e.g., open-ended questions). PA makes students grade their peers’ answers, and provides learners with significant advantages, such as refining their knowledge of the subject matter, and developing their meta-cognitive skills. In this work, we present a software platform called K-OpenAnswer, which helps teachers to simulate the dynamic of a MOOC where PA is used. The system uses a machine learning technique, based on a modified version of the K-NN algorithm, and provides teachers with a statistical environment by which they can monitor the evolving dynamic of a simulated MOOC, according to the techniques we use to implement PA. An experimental evaluation is presented that highlights the advantages of using the system as a valid tool for the study of real MOOCs
Monitoring Massive Open Online Courses (MOOC) During the Covid-19 Pandemic
No full textThe last two years have been characterized by an exponential growth in the use of the Internet as a working and learning tool, due to the “Covid-19” pandemic. The increase in smart working and distance learning have been some of the most striking effects. Cities have become more eco-sustainable: less pollution and better life quality. In this work we present a brief review of some data science platforms, which are useful to monitor the learning processes that take place in Massive Open Online Courses. Through these tools, teachers could find out useful information about the learning processes, by extracting it from the (big) data produced by students’ activities and stored in log files. To show the usefulness of such tools, we propose a very simple case study, showing how to extract strategic information from the log database produced by a course delivered via Moodle platform. The results of this case study strengthen our hypothesis on the utility of a data science approach for monitoring learning processes, especially in MOOCs
Q2A-II, a System to Support Peer Assessment on Homework: A Study on Four Years of Use
No full textAutomated assessment of homework assignments is a challenging topic in programming courses. For some years we have been using our Q2A-II system, that supports 1) automated grading of homework programs, and 2) formative peer assessment, performed by students on the algorithm descriptions they submit with the homework. Here we present some of the data we collected during 4 years of activity with Q2A-II, in the framework of a university course on Basics of Computer Programming. Each year, 4 homework were administered to 300–500 learners, with about 8.600 submissions (each made of program + algorithmic description), overall, and about 23.300 peer evaluations. On such data we propose several observations, aiming to rate the effectiveness of the initiative, in view of a more in depth analysis. On the algorithm descriptions we performed a basic textual categorization, using BERTopic-based text embedding topic extraction. The classification aim is exclusively to assess whether a text can or cannot be considered as an algorithm description: in two of the research questions we try to validate the classification and to see how different can be the behavior of the authors of such descriptions during the peer assessment activity
An Exploration of Open Source Small Language Models for Automated Assessment
No full textWe explore the classification and assessment capabilities of a selection of Open Source Small Language Models, on the specific task of evaluating learners' Descriptions of Algorithms. The algorithms are described in the framework of programming assignments, to which the learners in a class of Basics in Computer Programming have to answer. The task requires to 1) provide a program, in Python, to solve the assigned problem, 2) submit a description of the related algorithm, and 3) participate in a formative peer assessment session, over the submitted algorithms. Can a Language Model, be it small or large, produce an assessment for the algorithm descriptions? Rather than using any of the most famous, huge, and proprietary models, here we explore Small, Open Source based, Language Models, i.e. models that can be run on relatively small computers, and whose functions and training sources are provided openly. We produced a ground-truth evaluation of a large set of algorithm descriptions, taken from one year of use of the Q2A-II system. In this we used an 8-value scale, grading the usefulness of the description in a Peer Assessment session. Then we tested the agreement of the models assessments with such ground-truth. We also analysed whether a pre-emptive, automated, binary classification of the descriptions (as useless/useful for a Peer Assessment activity) would help the models to grade the usefulness of the description in a better way
Automated Analysis of Algorithm Descriptions Quality, Through Large Language Models
No full textIn this paper we propose a method to classify the students’ textual descriptions of algorithms. This work is based on a wealth of data (programming tasks, related algorithm descriptions, and Peer Assessment data), coming from 6 years of use of the system Q2A, in a “Fundamentals of Computer Programming” course, given at first year in our university’s Computer Science curriculum. The descriptions are submitted, as part of the answer to a computer programming task, through Q2A, and are subject to (formative) Peer Assessment. The proposed classification method aims to support the teacher on the analysis of the quite numerous students’ descriptions, in ours as well as in other similar systems. We 1) process the students’ submissions, by topic automated extraction (BERTopic) and by separate Large Language Models, 2) compute their degree of suitability as “algorithm description”, in a scale from BAD to GOOD, and 3) compare the obtained classification with those coming from the teacher’s direct assessment (expert: one of the authors), and from the Peer Assessment. The automated classification does correlate with both the expert classification and the grades given by the peers to the “clarity” of the descriptions. This result is encouraging in view of the production of a Q2A subsystem allowing the teacher to analyse the students’ submissions guided by an automated classification, and ultimately support fully automated grading
TutorChat: a Chatbot for the Support to Dyslexic Learner’s activity through Generative AI
No full textWe present TutorChat, an intelligent chatbot conceived to be able support search and synthesis of information during a learning task accomplishment, in particular for dyslexic students. TutorChat is based on ChatGPT; it is able to support question/answer inter-activity of learners, and to generate concept maps on the topics at hand, with the possibility, beside analysis, to have such maps extended with additional sub-maps starting from a selected concept. We let TutorChat be used by a sample of dyslexic learners, coming from different educational levels. Then we collected encouraging sample’s feedback, through a questionnaire, about appreciation of the system’s services, and perception of the usefulness coming from its use
Learning Analytics Models: A Brief Review
No full textThe users of the World Wide Web produce data continuously. This happens in varied areas such as trading on line, product ratings, support and use of services, and many more, comprising Distance Education. The ever increasing amount of such data can make analysis and extraction of meaningful information progressively harder, and sophisticated analysis techniques are to be used to extract added value from data. Many companies do collection and analysis of data with the purpose to develop their marketing strategies. In the field of education, and Distance Education in particular, data collected through online Learning Management Systems (LMSs) can provide a great resource, and a strong challenge, for the analysis of learning processes, the design of training paths, and the updating and personalization of learning environments. While, on the one hand, there is an increasing demand by educational institutions to measure, demonstrate, and improve the results achieved in distance learning, on the other hand the logic of traditional reporting included in LMS platforms does not satisfy that growing need. Learning Analytics is the answer to the need for optimization of learning through the techniques of analysis of data produced by learning processes, involving all stakeholders of the system. In this paper we show and discuss a brief state of the art of models of Learning Analytics presented in the literature
- …
