An empirical investigation of students’ perceptions of self-regulated learning in Online Blended Learning : a case study of a novel E-learning platform

Emerging online learning technology such as massive open online courses (MOOCs) is a new trend in learning technology. With the propagation of MOOC as a vast learning platform, very little has been known nowadays about the online blended learning systems and how it improves students’ performance. The blended classroom was conducted using traditional teaching method in a brick-and mortar classroom arrangement and online. The research focuses on the usefulness of blended classroom teaching for a single sample of first year undergraduate students (n = 27) in a computer security module. The sample students participated in this study in an online blended classroom incorporating the orthodox (traditional) class teaching methods. This research investigates the various techniques students used to motivate their studying habit. The computer security module was created in a novel e-learning platform known as eLDa. This is an online platform developed for the delivery of computing concepts, and python programming. This investigation aims at revealing students’ perceptions on self-regulated learning (SRL) skills. Multi-dimensional questionnaires were designed to collate sufficient data on the learning skills and the motivation of the students to study. These surveys analyse the following: (i) the various students’ patterns of motivation (ii) the manner of learning suitable to individual student (iii) the level of improvement attained. The research compared the new introduction of blended class seminar with an initial run of a previous cohort of a traditional class seminar on computer security module. The research approach expanded on an existing Online Selfregulated Learning Questionnaire (OSLQ) as the instrument for measuring the self-regulated learning skills. In order to collect the research data, hard copy questionnaires were distributed during the data collection process in two of the traditional face-to-face learning to obtain the students’ response. Descriptive statistical method was applied for the data analysis and evaluation using a statistical package for the social sciences (SPSS) tool. The results indicated the support received from the orthodox methods of teaching and the feedback received help in informing a better blended classroom delivery. The study analysis has provided insights to good practice with respect to the future direction of the online blended course embedded in the eLDaMOOC-learning platform. In summary, the blended learning used in this context was to introduce learners to the 21st century skills in learning, such as critical thinking skills, and self-regulated learning skills. Self-directed learning skills, we presume can lead and encourage learners to the era of autonomous e-learning in education

Trends in the Development of Basic Computer Education at Universities

Basic computer education in universities is experiencing huge problems. On the one hand, the amount of knowledge that a university graduate must have is increasing very quickly. On the other hand, the contingent of students varies greatly in terms of the level of training and motivation, and the level of this differentiation is constantly growing. As a result, the complexity of training and the percentage of dropouts increase. Scientists and educators are looking for a solution to these problems in the following areas: revising the knowledge necessary for obtaining at the university in the direction of the reality of their receipt in the allotted time; the use of new information technologies to simplify the learning process and improve its quality; development of the latest teaching methods that take into account the realities. This paper presents a strategic document in the field of computer education at universities - Computing Circulum 2020, as well as an overview of the areas of development of basic computer education, such as learning using artificial intelligence, virtual laboratories, microprocessor kits and robotics, WEB - systems for distance and blended learning, mobile application development, visual programming, gamification, computer architecture & organization, programming languages, learning technologies. In addition, the author gives his experience and vision of teaching basic computer education at universities

Blended Laboratory Design Using Raspberry Pi Pico for Digital Circuits and Systems

Raspberry Pi Pico, based on chip RP2040, is an easy-to-use development microcontroller board that can provide flexible input/output functions and meets the teaching needs of basic electronics to first-year university undergraduates. This article presents our blended laboratory design using Raspberry Pi Pico for the course unit Digital Circuits and Systems. Considering the impacts of Coronavirus Disease 2019 (COVID-19) and the reduced number of students attending the in-person laboratory, we provide an alternative approach using an online Raspberry Pi Pico simulator produced by Wokwi for those students who cannot attend the physical laboratory. The entire laboratory is designed by design-based learning pedagogical methodology and consists of three dependent sessions. Throughout the three laboratory sessions, first-year undergraduates are expected to understand the basic digital logic and electronic circuits by building a simplified interactive traffic light controller system using Raspberry Pi Pico and Python programming. The intended learning outcomes, full details of the blended laboratory design, and the laboratory design evaluation results are given and discussed in this article to verify the effectiveness of the blended laboratory design using Raspberry Pi Pico. By analyzing the empirical data collected from laboratory participants, the effectiveness of the proposed blended laboratory design can be well supported, and all intended learning outcomes are successfully achieved subject to the impacts of COVID-19

Learning to code in class with MOOCs: Process, factors and outcomes

Problem: Python became the most popular programming language in recent years, beating Java, the programming language still widely used as the main programming language in many undergraduate degrees on computer science related areas. Students from those degrees often do not get Python in their syllabus, but the job market is demanding it increasingly. Objective: To assess if learning a new programming language by following a MOOC is feasible in a fully dedicated mode and allows achieving a learning outcome comparable to the traditional in-class learning process. Proposal: Students from undergraduate degrees lacking Python skills followed a dedicated and intensive learning process on that language based on an in-class MOOC. The latter is suitable for students with some background in programming, as is the case, allowing a faster learning pace. Participants’ subjective perception of the corresponding workload was monitored. Validation: A programming contest, using an automatic judge, was used as a validation for this proposal. Two groups of students participated: those from three degrees lacking Python, which followed the proposed MOOC (experimental group), and those from the degree that includes Python programming, which had a traditional in-class learning process (control group). Conclusions: The experiment results were analysed and it was inferred that the proposed in-class MOOC learning approach is as effective as the traditional learning approach. Furthermore, it was identified that the students’ average grades obtained in the previous programming courses taken as part of their degree’s syllabus and the number of MOOC modules finished in the context of this experiment directly influence the number of points obtained in the contest.Problema: Nos últimos anos, Python tornou-se a linguagem de programação mais popular, ultrapassando o Java, que continua a sermuito usada como principal linguagem de programação em muitas licenciaturas relacionadas com informática. Estas licenciaturas acabam muitas vezes por não oferecer esta competência aos estudantes, no entanto o mercado de trabalho procura-a cada vez mais. Objectivo: Avaliar a possibilidade de aprender uma nova linguagem de programação através de um MOOC num regime de total dedicação. E por fim, perceber se este permite obter resultados comparáveis ao ensino tradicional. Proposta: Os estudantes com falta de conhecimentos de Python realizaram um processo de aprendizagem intensivo desta linguagem através de um MOOC em sala de aula. Este último é adequado a estudantes com alguns conhecimentos de programação, permitindo assim um ritmo mais rápido de aprendizagem. A perceção subjetiva dos participantes sobre a respetiva carga de trabalho foi monitorizada. Validação: Realização de um concurso de programação recorrendo a um juiz automático. Dois grupos de estudantes participaram neste concurso: estudantes das 3 licenciaturas sem conhecimentos de Python, que realizaram o MOOC (grupo experimental), e os estudantes da licenciatura que inclui Python e que teve uma aprendizagem tradicional (grupo de controlo). Conclusões: Os resultados deste experimento foram analisados e inferiu-se que a aprendizagem de um MOOC em sala de aula é tão eficaz quanto o ensino tradicional. Para além disso, foi também verificado que a média de notas dos estudantes obtida nas unidades curriculares de programação que já frequentaram no seu curso e o número de módulos feitos no MOOC no contexto desta experiência influenciam diretamente os pontos obtidos no concurso de programação

Bayesian Methods for Exoplanet Science

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.Comment: Invited revie