Learning in the workplace is crucial in higher engineering education, since it allows students to transfer knowledge and skills from university to professional engineering practice. Learning analytics endeavors in higher education have primarily focused on classroom-based learning. Recently, workplace learning analytics has become an emergent research area, with target users being workers, students and trainers. We propose technology for workplace learning analytics that allows program managers of higher engineering education programs to get insight into the workplace learning of their students, while ensuring privacy of students' personal data by design. Using a design-based agile methodology, we designed and developed a customizable workplace learning dashboard. From the evaluation with program managers in the computing domain, we can conclude that such technology is feasible and promising. The proposed technology was designed to be generalizable to other (engineering) domains. A next logical step would be to evaluate and improve the proposed technology within other engineering domains.
A promising contribution of Learning Analytics is the presentation of a learner's own learning behaviour and achievements via dashboards, often in comparison to peers, with the goal of improving self-regulated learning. However, there is a lack of empirical evidence on the impact of these dashboards and few designs are informed by theory. Many dashboard designs struggle to translate awareness of learning processes into actual self-regulated learning. In this study we investigate a Learning Analytics dashboard based on existing evidence on social comparison to support motivation, metacognition and academic achievement. Motivation plays a key role in whether learners will engage in self-regulated learning in the first place. Social comparison can be a significant driver in increasing motivation. We performed two randomised controlled interventions in different higher-education courses, one of which took place online due to the COVID-19 pandemic. Students were shown their current and predicted performance in a course alongside that of peers with similar goal grades. The sample of peers was selected in a way to elicit slight upward comparison. We found that the dashboard successfully promotes extrinsic motivation and leads to higher academic achievement, indicating an effect of dashboard exposure on learning behaviour, despite an absence of effects on metacognition. These results provide evidence that carefully designed social comparison, rooted in theory and empirical evidence, can be used to boost motivation and performance. Our dashboard is a successful example of how social comparison can be implemented in Learning Analytics Dashboards.
Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome.
Het lectoraat Applied Quantum Computing is een samenwerking tussen de Hogeschool van Amsterdam en het Centrum Wiskunde en Informatica. Dit lectoraat gaat zich bezig houden met het leggen van een verbinding tussen enerzijds fundamenteel onderzoek en anderzijds praktische problemen. In een samenwerking met IBM, Capgemini en Qusoft zullen cases en experimenten worden uitgevoerd hoe Quantum Computing bedrijven gaat beïnvloeden. Op het gebied van Quantum Communication zal onderzocht worden hoe m.b.v. Quantum Technologie gekomen kan worden tot een veilige communicatie. Ook zal aangesloten worden bij onderzoek naar en onderwijs worden ontwikkeld rondom hoe quantum mechanische effecten praktisch ingezet kunnen worden om metingen te verrichten. Onderzoek zal verricht worden naar het implementeren van theoretische oplossingen als bedacht in de laboratoria van universiteiten voor problemen bij bedrijven en instellingen. Binnen de Hogeschool van Amsterdam zal aansluiting worden gezocht met het onderzoek dat wordt gedaan binnen diverse lectoraten van de Faculteit DMCI, zoals responsible IT (i.o) en Urban Analytics en met de onderzoekers van de groep Urban Technology van de faculteit Techniek. In het onderwijs wordt een relatie bestendigd met opleidingen als HBO-ICT, waarvoor een minor wordt ontwikkeld, en Technische Natuurkunde. Daarbuiten zal verder gewerkt worden aan een netwerk om te komen tot een ecosysteem van instellingen en bedrijven. De Hogeschool van Amsterdam draagt Marten Teitsma als lector voor. Marten Teitsma heeft heeft veel ervaring in het onderwijs, ontwikkeling daarvan, als leidinggevende en is gepromoveerd in de Artificiële Intelligentie. Binnen de hogeschool heeft hij het initiatief genomen tot diverse activiteiten op het gebied van Quantum Computing.
Naast de grote voordelen van social media, zijn er ook risico’s. Jongeren moeten zich veilig kunnen voelen in het digitale domein. Daarom is het belangrijk dat ze leren wat de impact is van hatelijke, discriminerende en schadelijke berichten op social media en wat ze ertegen kunnen doen.Doel Doel van dit project is jongeren bewust maken van de negatieve effecten van online hate speech via video workshops en hen te leren om hate speech te herkennen en daar adequaat mee om te gaan. Resultaten Het project levert de volgende resultaten op: Lesstof en workshops over hate speech Een interactieve game over hate speech Een doorlopende leerlijn (praktijk – VWO, alle niveaus en leerjaren) over hate speech Looptijd 01 december 2019 - 30 juni 2022 Aanpak Na het uitvoeren van onderzoek naar online hate speech, wordt er lesmateriaal en een workshop ontwikkeld. Na het draaien van een pilot worden de workshops breed uitgerold bij in totaal zo’n 6000 leerlingen. Met behulp van learning analytics, observaties en interviews meten we het effect van de workshops. Cofinanciering Dit project wordt gefinancierd door het Ministerie van Justitie & Veiligheid.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
