A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments

The rapid evolution of technology directly impacts the skills and jobs needed in the next decade. Users can, intentionally or unintentionally, develop different skills by creating, interacting with, and consuming the content from online environments and portals where informal learning can emerge. These environments generate large amounts of data; therefore, big data can have a significant impact on education. Moreover, the educational landscape has been shifting from a focus on contents to a focus on competencies and capabilities that will prepare our society for an unknown future during the 21st century. Therefore, the main goal of this literature survey is to examine diverse technology-mediated environments that can generate rich data sets through the users’ interaction and where data can be used to explicitly or implicitly perform a data-driven evaluation of different competencies and capabilities. We thoroughly and comprehensively surveyed the state of the art to identify and analyse digital environments, the data they are producing and the capabilities they can measure and/or develop. Our survey revealed four key multimedia environments that include sites for content sharing & consumption, video games, online learning and social networks that fulfilled our goal. Moreover, different methods were used to measure a large array of diverse capabilities such as expertise, language proficiency and soft skills. Our results prove the potential of the data from diverse digital environments to support the development of lifelong and lifewide 21st-century capabilities for the future society

Analysis of Student Behavior and Score Prediction in Assistments Online Learning

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we identified important features from student activity data to improve grade prediction, helping identify at-risk students early and monitor their progress. Lastly, we used graph representation learning to understand complex relationships in the data, leading to more accurate predictive models. This research enhances our understanding of data mining in online learning, with implications for personalized learning and support mechanisms

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Decision Support Systems as the Bridge between Marketing Models and Marketing Practice

The field of marketing decision models emerged about fifty years ago. In the beginning, optimization techniques from the field of Operations Research (OR) were dominant, but soon, the modeling of marketing phenomena and marketing problems became interesting in itself, irrespective of whether they could be solved with a known OR technique. The field of marketing models developed its own identity and became an important academic field (Wierenga 2008b). Somewhat later the term "marketing science" became in vogue, as a close synonym to marketing models. In this current, first decade of the new Millennium, the field of marketing science is in excellent shape with booming journals and exponentially growing numbers of publications. However, a legitimate question can be asked: what is the impact of this growing body-of-knowledge on marketing practice? Does all this work lead to better marketing management decisions? In other words "Was macht die Wissenschaft fur die Unternehmenspraxis?" (Simon 2008). One important flow of marketing knowledge to marketing practice goes through marketing education and marketing literature .. The newest marketing insights are disseminated through courses, textbooks and other communication channels. But there is another important way of making the results from marketing science useful for marketing practice

Automotive UX design and data-driven development: Narrowing the gap to support practitioners

The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments). However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving. In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer. We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities. We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs. From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data. We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration. New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process

An exploration of student performance, utilization, and attitude to the use of a controlled content sequencing web based learning environment

Universities, traditionally places of teaching and research, have seen and are continuing to see radical changes occur in the area of teaching and the methods of teaching delivery. The World Wide Web, or ‘Web’ has begun to subsume the classroom as the preferred means by which students access their tertiary learning materials, and ultimately, how academic staff deliver those materials. The delivery of learning via the Web takes many forms and is generically, and usually inaccurately, referred to by such names as e-learning, online learning, web-based training and web based education, using such technologies as virtual learning environments, learning management systems and learning content management systems. This study focuses specifically on the delivery of electronic learning materials in the support of both inclass and online teaching

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Compared to traditional classroom learning, success in online learning tends to depend more on the learner’s skill to self-regulate. Self-regulation is a complex meta-cognitive skill set that can be acquired. This study explores the effectiveness of a virtual learning assistant in terms of (a) developmental, (b) general compensatory, and (c) differential compensatory effects on learners’ self-regulatory skills in a sample of N = 157 online learners using an experimental intervention-control group design. Methods employed include behavioural trace data as well as self-reporting measures. Participants provided demographic information and responded to a 24-item self-regulation questionnaire and a 20-item personality trait questionnaire. Results indicate that the adaptive assistance did not lead to substantial developmental shifts as captured in learners’ perceived levels of self-regulation. However, various patterns of behavioural changes emerged in response to the intervention. This suggests that the virtual learning assistant has the potential to help online learners effectively compensate for deficits (in contrast to developmental shifts) in self-regulatory skills that might not yet have been developed

Guidelines for the analysis of student web usage in support of primary educational objectives

The Internet and World Wide Web provides huge amounts of information to individuals with access to it. Information is an important driving factor of education and higher education has experienced massive adoption rates of information and communication technologies, and accessing the Web is not an uncommon practice within a higher educational institution. The Web provides numerous benefits and many students rely on the Web for information, communication and technical support. However, the immense amount of information available on the Web has brought about some negative side effects associated with abundant information. Whether the Web is a positive influence on students’ academic well-being within higher education is a difficult question to answer. To understand how the Web is used by students within a higher education institution is not an easy task. However, there are ways to understand the Web usage behaviour of students. Using established methods for gathering useful information from data produced by an institution, Web usage behaviours of students within a higher education institution could be analysed and presented. This dissertation presents guidance for analysing Web traffic within a higher educational institution in order to gain insight into the Web usage behaviours of students. This insight can provide educators with valuable information to bolster their decision-making capacity towards achieving their educational goals