Interpretation of computational thinking evaluation results for enrollment prediction

During two generations (2016 and 2017) the computational thinking evaluation has been carried out in order to establish learning scenarios for new students, such interventions have been made in the Programming methodology course, it belonging to the career of Information Technology at the Technological University of Puebla in México. The results have led a personalized education for students, recognizing previous skills as well as trying to correct those missing, so that it acquires the competences respective, credit the course and improve the retention percentage of the first quarter. In this sense, when detecting possible skill gaps, is it possible to predict what will be the impact to maintain or decrease enrollment during and the end of quarter? The present work aims to answer the question by the results interpretation obtained from the computational thinking evaluation to 242 new students, generation 2018. Initially, it was stablished which would be the student's situation during and the end of four months from September to December based on the correct assessment reagents; three categories were determined: 1. Sure desertion, 2. Safe permanence, 3. Variable permanence. Later, 50 students who enrolled the next quarter (January-April 2019) were revised if they had been predicted properly; using a survey, the familiarity of key concepts of the subject Programming methodology was obtained with the aim of determining a correspondence with the evaluation of computational thinking skills, as well as the established situation, consequently, establishing the validity of predicting the enrollment

Artificial intelligent based teaching and learning approaches: A comprehensive review

The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates

Curriculum –Releasing Maths Anxiety with the Use of Robotics

info:eu-repo/semantics/publishedVersio

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

NANO-MOOCs to train university professors in digital competences

[EN] Rapid changes in technology force Higher Education Institutions (HEIs) to generate policies and permanent digital adaptations in their exercise of forming professionals through university professors. HEIs -in their permanent desire to qualify teaching faculty and graduate high-level professionals- develop continuous training events to strengthen and update techno-pedagogical skills that allow giving concrete responses to the needs of a globalized society during a human-educational crisis that arises from the COVID-19 pandemic. This study aims at analyzing whether nano-MOOCs improve digital teaching competences in university professors since in the scientific literature, this topic does not show with certainty the effectiveness of these types of courses in teacher training. By conducting a quantitative descriptive-inferential, comparative quasi-experimental research (pre-test and post-test) and with a sample made up of 297 faculty members from Universidad Técnica del Norte (UTN, Ibarra-Ecuador) belonging to the five academic units that compose it, it was identified that the teaching staff has limitations in two of the areas of competence that are articulated by INTEF Common Framework: creation of digital content and security; nevertheless, they did show optimal skills in the areas of information and information literacy, communication and collaboration, and problem solving. The findings also determined that online training based on a nano-MOOC format becomes a successful alternative for university faculty training, 83.84% of the participants under study improved their level of digital competence. These results show that an efficient customizable training can be achieved in less time and adjusted to the needs and characteristics of the professors. The criteria of various authors in this field are ratified with this research, it is, therefore, relevant to evaluate the level of digital competence of teachers and, based on that, be able to plan a personalized training program