Virtual pedagogical model: development scenarios

info:eu-repo/semantics/publishedVersio

Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

Assessing Business Learning by Analysing ERP Simulation Log Files

Business education is facing increasing pressures to equip graduates with both practical competencies and functional knowledge. In addition to developing authentic learning environments where one can learn those competencies, we need to develop authentic assessment methods. Computer-assisted learning environments, such as business games and simulations, assist in achieving the intricate learning goals, and at the same time, provide copious quantities of data. In this paper, we present an authentic assessment approach to measure the students’ practical hands-on activities rather than their theoretical knowledge. We analysed the log file data of an ERP-supported simulation to assess learning in a full year case study with first year BBA students. The analysis firstly demonstrates how and when log files can be used, and secondly indicated positive learning results on the cognitive and psychomotor domains of Bloom’s taxonomy. The log file analysis holds potential particularly for formative assessment to guide the student’s learning process during the simulation. These findings and our lessons learned can be applied to assessing learning in computer-supported learning environments, particularly in business simulations

Conducting Synchronous Assessment through Web Videoconference to Improve Online Learning: Case Outcomes with Nonparametric Analysis

Online assessment has always been a challenge to online teaching. Educators have been exploring a variety of methods to perform online assessment. However, it appears that there is not enough work in the field focusing on online synchronous assessment. This paper presents two cases that demonstrate the design and implementation of using web videoconference for synchronous assessment in an educational research methods online course and an instructional video production online course. The purpose of the two cases was to explore whether or with what methods student online learning could be improved through synchronous assessment. Case outcomes were analyzed with nonparametric methods, and the results did show students’ improvement in their learning, specifically in their understanding and mastering of factual, conceptual, procedural and metacognitive knowledge. Methods, procedures, tips and cautions of conducting such videoconference-based synchronous assessment in online courses are discussed

The Role Artificial Intelligence in Modern Banking: An Exploration of AI-Driven Approaches for Enhanced Fraud Prevention, Risk Management, and Regulatory Compliance

Banking fraud prevention and risk management are paramount in the modern financial landscape, and the integration of Artificial Intelligence (AI) offers a promising avenue for advancements in these areas. This research delves into the multifaceted applications of AI in detecting, preventing, and managing fraudulent activities within the banking sector. Traditional fraud detection systems, predominantly rule-based, often fall short in real-time detection capabilities. In contrast, AI can swiftly analyze extensive transactional data, pinpointing anomalies and potentially fraudulent activities as they transpire. One of the standout methodologies includes the use of deep learning, particularly neural networks, which, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision.  Furthermore, the enhancement of Know Your Customer (KYC) processes is achievable through Natural Language Processing (NLP), where AI scrutinizes textual data from various sources, ensuring customer authenticity. Graph analytics offers a unique perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers indicative of money laundering. Predictive analytics, transcending traditional credit scoring methods, incorporates a diverse data set, offering a more comprehensive insight into a customer's creditworthiness.  The research also underscores the importance of user-friendly interfaces like AI-powered chatbots for immediate reporting of suspicious activities and the integration of advanced biometric verifications, including facial and voice recognition. Geospatial analysis and behavioral biometrics further bolster security by analyzing transaction locations and user interaction patterns, respectively.  A significant advantage of AI lies in its adaptability. Self-learning systems ensure that as fraudulent tactics evolve, the AI mechanisms remain updated, maintaining their efficacy. This adaptability extends to phishing detection, IoT integration, and cross-channel analysis, providing a comprehensive defense against multifaceted fraudulent attempts. Moreover, AI's capability to simulate economic scenarios aids in proactive risk management, while its ability to ensure regulatory compliance automates and streamlines a traditionally cumbersome process

An Active, Reflective Learning Cycle for E-Commerce Classes: Learning about E-commerce by Doing and Teaching

Active, experiential learning is an important component in information systems education, ensuring that students gain an ap-preciation for both practical and theoretical information systems concepts. Typically, students in active, experiential classes engage in real world projects for commercial companies or not-for-profit organizations. In the latter case, such engagements are often referred to as ‘service learning’ or ‘community-engaged education’. In this paper, we describe a novel capstone in-formation systems class where, instead of undertaking a conventional single-team, single-project experiential engagement, the students initiated a fully-fledged new not-for-profit organization from the ground up. The not-for-profit organization, The Online Business Guidebook, was founded with the mission of providing public education on how to start and grow an online business. In an unusual twist on a typical e-commerce class, the students both implemented e-commerce technologies (“active learning by doing”) and created and disseminated e-commerce training materials (“reflective learning / learning by teaching”), rather than solely being recipients of instructional resources. This paper describes the manner in which this class was run, the learning outcomes set and evaluation methods used, problems encountered, and recommendations. We propose a replicable model and specific learning outcomes for information systems educators who wish to teach e-commerce classes with an active and reflective pedagogical approach