Offline versus Online Learning

While there have been numerous studies focusing on finding students' perception on online learning, the studies regarding the perception on both online and offline learning have generally been overlooked. This study, therefore, attempted to fill this void. The main objective is to find out whether offline learning is more preferable than online learning. This descriptive research employed questionnaire with Likert scale through online google forms in collecting the data. 36 students from Biology Department, Universitas Negeri Padang participated in this research. The respondents were asked some questions regarding learning implementation, lecturer's competency and facilities to know their perception. The research found that the students generally showed more positive attitude towards offline learning which can be perceived from the comparison of percentagesin each question. The findings then suggest that students preferred offline learning to online learning and hence, offline learning is more recommended to be conducted

Online Learning vs. Offline Learning in an MIS Course: Learning Outcomes, Readiness, and Suggestions for the Post-COVID-19 World

This study aims to compare learning outcomes and technology readiness in online versus offline learning and to find suggestions from the viewpoint of learners. Besides, this study also compares differences in students’ perceptions among learning styles. The associations between several factors such as experience and gender, and learning preferences are also explored. A questionnaire was developed to gather data from students who enrolled in an MIS course during the middle of the COVID-19 pandemic. Around half of the students were assigned to study the topic ‘using MS Excel basics’ in offline sessions, whereas the rest were assigned to learn through recorded videos online. Responses from 44 students, together with their comments and suggestions, were used for data analysis. This study found that both online and offline delivery methods can improve students’ cognitive processes according to the Revised Bloom’s Taxonomy and their topic interest significantly. On-campus classes could significantly enhance students’ class attendance intention, but online classes could not. The cognitive process of RBT in terms of evaluating MS Excel content and class attendance intention of online students were significantly lower than offline students. Students also felt that place, equipment, and software on-campus were more ready than online environments. This work provides guidelines for both lecturers and universities in choosing teaching methods for using basic tools after the COVID-19 situation pass, selecting proper course types, designing course activities, and providing sufficient supports for better online learning outcomes. Research gaps suggested by past studies are filled up in this study

Mirror reversal and visual rotation are learned and consolidated via separate mechanisms: recalibrating or learning de novo?

Motor learning tasks are often classified into adaptation tasks, which involve the recalibration of an existing control policy (the mapping that determines both feedforward and feedback commands), and skill-learning tasks, requiring the acquisition of new control policies. We show here that this distinction also applies to two different visuomotor transformations during reaching in humans: Mirror-reversal (left-right reversal over a mid-sagittal axis) of visual feedback versus rotation of visual feedback around the movement origin. During mirror-reversal learning, correct movement initiation (feedforward commands) and online corrections (feedback responses) were only generated at longer latencies. The earliest responses were directed into a nonmirrored direction, even after two training sessions. In contrast, for visual rotation learning, no dependency of directional error on reaction time emerged, and fast feedback responses to visual displacements of the cursor were immediately adapted. These results suggest that the motor system acquires a new control policy for mirror reversal, which initially requires extra processing time, while it recalibrates an existing control policy for visual rotations, exploiting established fast computational processes. Importantly, memory for visual rotation decayed between sessions, whereas memory for mirror reversals showed offline gains, leading to better performance at the beginning of the second session than in the end of the first. With shifts in time-accuracy tradeoff and offline gains, mirror-reversal learning shares common features with other skill-learning tasks. We suggest that different neuronal mechanisms underlie the recalibration of an existing versus acquisition of a new control policy and that offline gains between sessions are a characteristic of latter

The development of METAKU to support learning in hypermedia environment

The development of learning skills is not given attention in many classrooms in all levels of education in our education system today. We assume that students will be able to develop their own learning skills. The challenges students face in hypermedia learning environment need to be considered to help them learn effectively in this type of learning environment. METAKU is developed to assist students to apply metacognitive learning strategies, that is, planning, monitoring and evaluation during the learning process in hypermedia learning environment. This paper discusses the development of METAKU which employed the first three stages of a generic instructional design model - ADDIE which consists of the following stages: 1)Analysis, 2) Design, 3) Development, 4) Implementation and 5) Evaluation. In the analysis stage, the data was collected using a triangulation method done concurrently: a survey of student’s preference of studying online versus offline; a focus group interview to identify challenges they face and strategies they use whilst accessing and studying the hypertext materials; and a record of student’s interaction with the computer using captivation software. A total of 240 second year university students in two public universities in Malaysia were involved in this study. The analysis stage provides information for stage 2 and 3 where the data collected was used in formulating the content and design of METAKU. It is hoped that METAKU will be able to help students develop learning skills in hypermedia learning environment

Online Learning From Input Versus Offline Memory Evolution in Adult Word Learning: Effects of Neighborhood Density and Phonologically Related Practice

This is the author's accepted manuscript. The original publication is available at http://jslhr.pubs.asha.org/article.aspx?articleid=1851189Purpose. This study investigates adult word learning to determine how neighborhood density and practice across phonologically-related training sets influence on-line learning from input during training versus off-line memory evolution during no-training gaps. Method. Sixty-one adults were randomly assigned to learn low or high density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Results. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high density and second set words than for low density and first set words. Conclusion. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate on-line learning from input but seems to impede off-line memory evolution

Comparison of Student Performance in Online versus Offline Teaching: A Case-Control Study in Obstetrics and Gynaecology Lectures in the Qassim Region, Saudi Arabia

Introduction: During the Coronavirus Disease 2019 (COVID19) pandemic, there was a global need for online learning. Numerous publications were observed both in favour of and against online platforms, but most of them were qualitative. Now that the pandemic is over, we aimed to explore the effectiveness of online teaching compared to offline teaching. Aim: This study aimed to descriptively compare the performance of medical students in online versus offline class teaching of the Obstetrics and Gynaecology course. Materials and Methods: This quantitative retrospective casecontrol study was conducted in the Department of Obstetrics and Gynaecology at College of Medicine, Qassim University, from July 2022 to January 2023. The academic year 2018/2019 was considered the control group (August 2018 to June 2019, offline teaching), and the year 2020/2021 was considered the case group (online teaching, August 2020 to June 2021). A total of 123 students had the Obstetrics and Gynaecology course delivered online, compared to a control group of 115 students who had the same course offline. Chi-square test was applied to analyse categorical variables, considering a p-value <0.05 as significant. Results: The measured outcomes included overall students’ performance in terms of marks and grades, as well as performance in relation to gender and attendance rates. Overall, students’ grades and attendance were higher in the online group (p=0.004 and p=0.03, respectively), which was more evident among male students (p=0.009). Conclusion: The findings suggest that medical students’ performance in online learning is comparable to or better than face-to-face teaching. Further research is needed to explore the performance of male students compared to female students

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. The supplementary video can be viewed at https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a \emph{replay} methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM 2011 conferenc