Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Autonomous Exchanges: Human-Machine Autonomy in the Automated Media Economy

Contemporary discourses and representations of automation stress the impending “autonomy” of automated technologies. From pop culture depictions to corporate white papers, the notion of autonomous technologies tends to enliven dystopic fears about the threat to human autonomy or utopian potentials to help humans experience unrealized forms of autonomy. This project offers a more nuanced perspective, rejecting contemporary notions of automation as inevitably vanquishing or enhancing human autonomy. Through a discursive analysis of industrial “deep texts” that offer considerable insights into the material development of automated media technologies, I argue for contemporary automation to be understood as a field for the exchange of autonomy, a human-machine autonomy in which autonomy is exchanged as cultural and economic value. Human-machine autonomy is a shared condition among humans and intelligent machines shaped by economic, legal, and political paradigms with a stake in the cultural uses of automated media technologies. By understanding human-machine autonomy, this project illuminates complications of autonomy emerging from interactions with automated media technologies across a range of cultural contexts

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Ideological Misalignment in the Discourse(s) of Higher Education: Comparing University Mission Statements with Texts from Commercial Learning Analytics Providers

This study analyzes, interprets, and compares texts from different educational discourses. Using the Critical Discourse Analysis method, I reveal how texts from university mission statements and from commercial learning analytics providers communicate and construct different ideologies. To support this analysis, I explore literature strands related to public higher education in America and the emerging field of study and practice called learning analytics. Learning analytics is the administrative, research, and instructional use of large sets of digital data that are associated with and generated by students. The data in question may be generated by incidental online activity, and it may be correlated with a host of other data related to student demographics or academic performance. The intention behind educational data systems is to find ways to use data to “optimize” instructional materials and practices by tailoring them to perceived student needs and behaviors, and to trigger “interventions” ranging from warning messages to prescribed courses of study. The use of data in this way raises questions about how such practices relate to the goals and ideals of higher education, especially as these data systems employ similar theories and techniques as those used by corporate juggernauts such as Facebook and Google. Questions not only related to privacy and ownership but also related to how learning, education, and the purpose of higher education are characterized, discussed, and defined in various discourses are explored in this study

Pre-entry Selection Assessment Results and Final Degree Outcomes of Occupational Therapy Students: Are There Relationships?

Occupational therapy (OT) admissions tutors are tasked with ensuring the recruitment of high-quality candidates who can demonstrate both academic and professional skills throughout their education and subsequently into practice. While standardized admissions criteria do not exist, it is widely accepted that both cognitive and non-cognitive characteristics should be assessed to establish academic and professional qualities of applicants. Pre-admission qualifications are generally classed as a reliable assessment of academic (cognitive) ability, with literature supporting positive but variable relationships between academic qualifications and degree classifications. Evidence to support trusted assessments of pre-admission professional (non-cognitive) skills is lacking, with ambiguity surrounding relationships between pre-admission results and graduation data. This paper compares outcomes of cumulative cognitive and non-cognitive assessments for one cohort (n=44) of pre-registration BSc (Hons) OT students at one United Kingdom (UK) university, with final degree outcomes. Quantitative methods were employed to explore potential relationships between numerical pre-admission and graduation data. Correlations were not found between a) pre-entry qualifications and final degree classifications or b) selection assessment scores and final degree classifications. Nine candidates did not meet the university’s minimum cognitive (n=3) or non-cognitive criteria (n=6) but were granted admission. All nine went on to graduate with a range of degree classifications preventing a loss to the university and OT profession. Results demonstrate pre-entry data is not necessarily a predictor of graduation outcomes, indicating further research into the variables which impact a student’s journey between admission and graduation is warranted