e‑Learning Success Model: An Information Systems Perspective

This paper reports the observations made and experience gained from developing and delivering an online quantitative methods course for Business undergraduates. Inspired by issues and challenges experienced in developing the online course, a model is advanced to address the question of how to guide the design, development, and delivery of successful e‑learning initiatives based on theories of a user‑centered information systems development paradigm. The benefits of using the proposed model for e‑learning success assessment is demonstrated through four cycles of action research after two action research cycles of pilot study. Findings from our empirical study confirm the value of an action research methodology for promoting e‑learning success. The paper concludes with a discussion on the merits of the proposed model in furthering our understanding of how to define, assess, and promote e‑learning success

Research in Economic Education: Five New Initiatives

Research is essential for improvement in teaching and learning economics. William E. Becker et al. (1991) called for new research on the relative merits of multiple-choice and essay tests, on the lasting effects of course work in economics, and on the effects of instructors, instructional techniques, and new technologies on student learning. To respond to the call, the Committee on Economic Education (CEE) of the American Economic Association recruited Robin Bartlett, William Becker, W. Lee Hansen, Peter Kennedy, and the authors to organize a conference that would jump-start new research projects

Impact of E-Learning on Indian Students during Pandemic: A Conceptual Approach

For the enhancement of distant education and learning through web the practice of e-learning has initiated in late 1990s Because of the significant growth of information and technology the adoption of e-learning became famous globally in late 2000s By the mid of 2010s utilization of electronic devices computers laptops mobile phones and tablets which supports internet connectivity were prominently used by Indian educational institutions to facilitate e-learning process to educate the students through online platforms In the later part of 2010 Indian organizations in collaboration with foreign companies developed their own web-based software and application in order to persuade the practice of e-learning for the students With reference to traditional learning e-learning was adopted in a few numbers Since the outbreak of pandemic issue Covid-19 lockdown and pandemic protocols leads to closure of schools and colleges across the country As a result educating the students through online e-learning served as the full time replacement for traditional learning Although educating students through e-learning made an appropriate replacement for traditional learning students across many parts of India subjected to confront certain issues as well as merits while learning through online On consideration with perception of students the article is aimed at investigating the positive and negative attributes of e-learning among Indian students based on the existing literary work

Learning Adaptive Display Exposure for Real-Time Advertising

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201

Moving outside the box: Researching e-learning in disruptive times

Indexación: Scopus.The rise of technology’s influence in a cross-section of fields within formal education, not to mention in the broader social world, has given rise to new forms in the way we view learning, i.e. what constitutes valid knowledge and how we arrive at that knowledge. Some scholars have claimed that technology is but a tool to support the meaning-making that lies at the root of knowledge production while others argue that technology is increasingly and inextricably intertwined not just with knowledge construction but with changes to knowledge makers themselves. Regardless which side one stands in this growing debate, it is difficult to deny that the processes we use to research learning supported by technology in order to understand these growing intricacies, have profound implications. In this paper, my aim is to argue and defend a call in the research on ICT for a critical reflective approach to researching technology use. Using examples from qualitative research in e-learning I have conducted on three continents over 15 years, and in diverse educational contexts, I seek to unravel the means and justification for research approaches that can lead to closing the gap between research and practice. These studies combined with those from a cross-disciplinary array of fields support the promotion of a research paradigm that examines the socio-cultural contexts of learning with ICT, at a time that coincides with technology becoming a social networking facilitator. Beyond the examples and justification of the merits and power of qualitative research to uncover the stories that matter in these socially embodied e-learning contexts, I discuss the methodologically and ethically charged decisions using emerging affordances of technology for analyzing and representing results, including visual ethnography. The implications both for the consumers and producers of research of moving outside the box of established research practices are yet unfathomable but excitinghttp://www.ejel.org/volume15/issue1/p5

A Comparative Study between Traditional Learning and E-Learning

This paper presents a comparative study between Traditional and E-learning. Traditional learning involves a physical place where students and teacher can interact whereas E-learning is pursued in an e-space where a server and internet browsing interface is to be there. Due to a constant trend of growing student numbers across the world traditional learning will be expensive. The reason behind is the physical engagement of a teacher in this method which involves payment to the teacher for his service and other required support assets. E-learning can be a method which can ensure reduced cost while enhancing the outcome in the learning system. It is based on wireless communication networks which are widely and rapidly used due to the flexibility, freedom to use that it promotes an effective learning system. The wireless communication allows learner to receive the learning materials and lectures from anywhere as long as they are connected to the internet. Classroom education may not always succeed for online learning, when the instructor is not around which need to stimulate motivation and continual learning progress. In this paper we also discuss the merits and demerits of both the methods with the help of statistical data analysis

Learning from assessment: evaluating the benefits of DALI (diagnostic assessment learning interface)

The DALI Project is a Teaching and Learning Innovation Fund (TALIF) pilot study at the University of Essex. DALI (Diagnostic Assessment Learning Interface) is an add-on to QuestionMark Perception (1), which enables students to see how well they’ve performed in each topic (a group of questions). It provides learners and instructors with multiple ways to view assessment information in order to gauge progress in specific topic areas. The DALI interface: allows a student to select the score level that they wish to achieve; highlights the last topic-based feedback provided to the learner; provides statistics associated with any selected topic; and displays topic descriptions as learning objectives. The paper discusses how the project aimed to understand better the use of online assessment with a particular focus on the student experience. Data were collected on matters such as what the students perceive to be the merits and demerits of online assessment, and what motivated or discouraged them from using it. These issues are particularly salient: the National Student Survey has shown assessment and feedback to be a particular concern for students. As the market for e-delivery expands more thought needs to be given to how students learn with e-learning, and the ways in which this should inform the design of elearning activity, including e-assessment

Learning Object Metadata and its application

A number of international efforts have been initiated during the past few years leading to the evolvement of various educational metadata specifications for the commonly agreed description of educational resources. Educational metadata can significantly enhance the effective description, search and retrieval of learning objects resulting in efficient organization of educational resources for technology supported instruction. As more and more applications are implemented using educational metadata, it becomes obvious that it would be difficult for a single metadata model to accommodate the functional requirements of all applications. This paper focuses on different existing educational metadata standards with the relative merits of each one, it will also examine the fundamental elements or basic structure of each one of the existing standards, and discuss the interoperability issues. Because of the various E-learning metadata standards that exist, interoperability is a major issue. A major barrier limiting system’s interoperability is the use of different specifications that define the structure and content of learning objects

Rapid Production of E-Learning Materials with Reusable Learning Objects: Experiences from the Global Academy for Extension Practice

Open educational resources, institutional collaborations, and content reusability approaches have been quickly emerging to minimize the time and money spent on developing e-learning materials. Reusing content with reusable learning objects (RLOs) is carving a new path for research on reusing and repurposing available high quality e-learning content. Prior research shows that this component-based approach best fits how educators prefer to access materials. In this paper, without arguing the merits and demerits of RLOs as a concept, the authors present an effective and affordable approach to creating e-learning materials with RLOs. The authors also present how they have implemented the proposed RLO approach in converting learning modules of the Global Academy for Extension Practice into multiple e-learning material formats

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning

Federated learning introduces a novel approach to training machine learning (ML) models on distributed data while preserving user's data privacy. This is done by distributing the model to clients to perform training on their local data and computing the final model at a central server. To prevent any data leakage from the local model updates, various works with focus on secure aggregation for privacy preserving federated learning have been proposed. Despite their merits, most of the existing protocols still incur high communication and computation overhead on the participating entities and might not be optimized to efficiently handle the large update vectors for ML models. In this paper, we present E-seaML, a novel secure aggregation protocol with high communication and computation efficiency. E-seaML only requires one round of communication in the aggregation phase and it is up to 318x and 1224x faster for the user and the server (respectively) as compared to its most efficient counterpart. E-seaML also allows for efficiently verifying the integrity of the final model by allowing the aggregation server to generate a proof of honest aggregation for the participating users. This high efficiency and versatility is achieved by extending (and weakening) the assumption of the existing works on the set of honest parties (i.e., users) to a set of assisting nodes. Therefore, we assume a set of assisting nodes which assist the aggregation server in the aggregation process. We also discuss, given the minimal computation and communication overhead on the assisting nodes, how one could assume a set of rotating users to as assisting nodes in each iteration. We provide the open-sourced implementation of E-seaML for public verifiability and testing