Teachers' Experience of Teaching and Online Learning Via WhatsApp as a Combination of Interactive English Learning media in the Covid-19 Pandemic Era of UNU Lab Elementary School Students in Blitar

English learning in elementary schools should be engaging, interactive and fun so that students have an interest and motivation for learning especially in the current Covid-19 pandemic, but in reality, the learning patterns in SD Lab Blitar UNU still do not apply English learning patterns that are interactive and fun online because teachers have not affected the learning model that suits students' needs. They find it difficult to determine the right online media as a learning medium due to locations that do not support using the full online facility. The purpose of this study is that the authors intend to provide solutions in formulating exciting and interactive patterns of English teaching to all teachers and students during the co-19 pandemic under online learning conditions. The author uses a qualitative approach through the case study application and applies self-selection to select audiences and conduct online interviews to retrieve all data in the completeness of the study. The results revealed that Teachers' Experience of Teaching and Online Learning Via WhatsApp could create Interactive English Learning in the Covid-19 Pandemic Era, and the Combination can attract students' interest in learning online

Crafting a rich and personal blending learning environment: an institutional case study from a STEM perspective

Institutional pressures to make optimal use of lecture halls and classrooms can be powerful motivators to identify resources to develop technology enhanced learning approaches to traditional curricula. From the academic’s perspective, engaging students in active learning and reducing the academic workload are important and complementary drivers. This paper presents a case study of a curriculum development exercise undertaken in a STEM subject area at a research-intensive UK university. A multi-skilled team of academics and learning designers have worked collaboratively to build this module which will be realised as a mix of online and face to face activities. Since the module addresses professional issues, a strong emphasis is being placed on establishing authentic learning activities and realistic use of prominent social tools.The learning designers are working for a cross-institutional initiative to support educational innovations; therefore it is important to carefully document the development process and to identify reusable design patterns which can be easily explained to other academics.<br/

Pembelajaran Ekonomi Generasi Z Di Masa Pandemi Covid-19

The objectives of this study are 1) Analyze the appropriate form of economic learning for generation Z; 2) Describe the appropriate learning techniques in the pandemic era for generation Z; 3) Compare the economic learning patterns before and after the Covid 19 pandemic;. The research method used is qualitative, with a descriptive approach using case studies in junior high schools (SMP), high schools (SMA) and universities in East Java province. The results show that 1) the right form of learning in generation Z is learning that utilizes digital technology whether it is carried out online, offline or blended learning. 2) Learning techniques in the pandemic era for generation Z are through recording, individual assignments ( self observation), projects / works, quizzes, live books, and group assignments. 3) Comparison of economic learning before the pandemic was carried out directly, while online-based economic learning after the Covid 19 pandemic was mostly conducted through the Learning Management System (LMS) and video conferencing. DOI: https://dx.doi.org/10.17977/UM014v14i12021p8

Design patterns for promoting peer interaction in discussion forums in MOOCs

Design patterns are a way of sharing evidence-based solutions to educational design problems. The design patterns presented in this paper were produced through a series of workshops, which aimed to identify Massive Open Online Course (MOOC) design principles from workshop participants’ experiences of designing, teaching and learning on these courses. MOOCs present a challenge for the existing pedagogy of online learning, particularly as it relates to promoting peer interaction and discussion. MOOC cohort sizes, participation patterns and diversity of learners mean that discussions can remain superficial, become difficult to navigate, or never develop beyond isolated posts. In addition, MOOC platforms may not provide sufficient tools to support moderation. This paper draws on four case studies of designing and teaching on a range of MOOCs presenting seven design narratives relating to the experience in these MOOCs. Evidence presented in the narratives is abstracted in the form of three design patterns created through a collaborative process using techniques similar to those used in collective autoethnography. The patterns: “Special Interest Discussions”, “Celebrity Touch” and “Look and Engage”, draw together shared lessons and present possible solutions to the problem of creating, managing and facilitating meaningful discussion in MOOCs through the careful use of staged learning activities and facilitation strategies

The Enactment of Online Learning for Special Need Students during COVID 19 Pandemic: A Case Study

The present study focuses on the implement of online learning in special education and how Information and Telecommunications Technology being used to strengthen and support the achievement of learning and the learning goals at special needs junior high school students (SMPLB) Jambi. The research method used in this study is a case study where the researchers observe how the patterns and processes of online education in the learning of SMPLB students, distributing questionnaires on the use of ICT to the teachers, interviews with parents/guardians of the students, and interviews with special junior high school teacher Sri Soedewi Maschun Sofyan, S.H Jambi. The results of the research indicate that the students in special needs schools have difficulty in obtaining technology that suits with their needs in learning. The learning process cannot be carried out as expected because the teachers have difficulty in making technology-based learning materials for the students, especially for students with the deaf and the blind. Reading and writing skills are also very limited, and the student learning is very low because the online method greatly limits opportunities for direct interaction between the students and the teachers. The involvement of parental assistance is also very low because the parents do not understand how to aid with children in online learning

Secretary and Online Matching Problems with Machine Learned Advice

The classical analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. Often this is not an issue with machine learning approaches, which shine in exploiting patterns in past inputs in order to predict the future. However, such predictions, although usually accurate, can be arbitrarily poor. Inspired by a recent line of work, we augment three well-known online settings with machine learned predictions about the future, and develop algorithms that take them into account. In particular, we study the following online selection problems: (i) the classical secretary problem, (ii) online bipartite matching and (iii) the graphic matroid secretary problem. Our algorithms still come with a worst-case performance guarantee in the case that predictions are subpar while obtaining an improved competitive ratio (over the best-known classical online algorithm for each problem) when the predictions are sufficiently accurate. For each algorithm, we establish a trade-off between the competitive ratios obtained in the two respective cases

Secretary and Online Matching Problems with Machine Learned Advice

The classical analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. Often this is not an issue with machine learning approaches, which shine in exploiting patterns in past inputs in order to predict the future. However, such predictions, although usually accurate, can be arbitrarily poor. Inspired by a recent line of work, we augment three well-known online settings with machine learned predictions about the future, and develop algorithms that take them into account. In particular, we study the following online selection problems: (i) the classical secretary problem, (ii) online bipartite matching and (iii) the graphic matroid secretary problem. Our algorithms still come with a worst-case performance guarantee in the case that predictions are subpar while obtaining an improved competitive ratio (over the best-known classical online algorithm for each problem) when the predictions are sufficiently accurate. For each algorithm, we establish a trade-off between the competitive ratios obtained in the two respective cases

Learning Decentralized Linear Quadratic Regulator with T\sqrt{T} Regret

We study the problem of learning decentralized linear quadratic regulator when the system model is unknown a priori. We propose an online learning algorithm that adaptively designs a control policy as new data samples from a single system trajectory become available. Our algorithm design uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. We show that our controller enjoys an expected regret that scales as $\sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern. For more general information patterns, the optimal controller is unknown even if the system model is known. In this case, the regret of our controller is shown with respect to a linear sub-optimal controller. We validate our theoretical findings using numerical experiments