Assessing the validity of a learning analytics expectation instrument: A multinational study

To assist Higher Education Institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (QSELA) was developed. This instrument contains 12 items, which are explained by a purported two-factor structure of Ethical and Privacy Expectations and Service Expectations. As it stands, however, the QSELA has only been validated with students from UK University students, which is problematic on account of the interest in Learning Analytics extending beyond this context. Thus, the aim of the current work was to assess whether the translated QSELA can be validated in three contexts (an Estonian, a Spanish, and a Dutch University). The findings show that the model provided acceptable fits in both the Spanish and Dutch samples, but was not supported in the Estonian student sample. In addition, an assessment of local fit is undertaken for each sample, which provides important points that need to be considered in future work. Finally, a general comparison of expectations across contexts is undertaken, which are discussed in relation to the General Data Protection Regulation (GDPR, 2018)

Challenges in context-aware mobile language learning: the MASELTOV approach

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

Characterization of shape and dimensional accuracy of incrementally formed titanium sheet parts with intermediate curvatures between two feature types

Single point incremental forming (SPIF) is a relatively new manufacturing process that has been recently used to form medical grade titanium sheets for implant devices. However, one limitation of the SPIF process may be characterized by dimensional inaccuracies of the final part as compared with the original designed part model. Elimination of these inaccuracies is critical to forming medical implants to meet required tolerances. Prior work on accuracy characterization has shown that feature behavior is important in predicting accuracy. In this study, a set of basic geometric shapes consisting of ruled and freeform features were formed using SPIF to characterize the dimensional inaccuracies of grade 1 titanium sheet parts. Response surface functions using multivariate adaptive regression splines (MARS) are then generated to model the deviations at individual vertices of the STL model of the part as a function of geometric shape parameters such as curvature, depth, distance to feature borders, wall angle, etc. The generated response functions are further used to predict dimensional deviations in a specific clinical implant case where the curvatures in the part lie between that of ruled features and freeform features. It is shown that a mixed-MARS response surface model using a weighted average of the ruled and freeform surface models can be used for such a case to improve the mean prediction accuracy within ±0.5 mm. The predicted deviations show a reasonable match with the actual formed shape for the implant case and are used to generate optimized tool paths for minimized shape and dimensional inaccuracy. Further, an implant part is then made using the accuracy characterization functions for improved accuracy. The results show an improvement in shape and dimensional accuracy of incrementally formed titanium medical implants

Higher education analytics: New trends in program assessments

© 2018, Springer International Publishing AG, part of Springer Nature. End of course evaluations technologies can provide critical analytics that can be used to improve the academic outcomes of almost any university. This paper presents key findings from a study conducted on more than twenty different academic degree-programs, regarding their use of end of course evaluation technology. Data was collected from an online survey instrument, in-depth interviews with academic administrators, and two case studies, one in the US and another in the UAE. The study reveals new trends including sectioning and categorization; questions standardization and benchmarking; alignment with key performance indicators and key learning outcomes; and grouping by course, program outcome, program, college, etc. in addition to those vertical structures, higher education institutions are vertically examining a specific question(s) across

Effect of stress relieving heat treatment on surface topography and dimensional accuracy of incrementally formed grade 1 titanium sheet parts

The forming of parts with an optimized surface roughness and high dimensional accuracy is important in many applications of incremental sheet forming (ISF). To realize this, the effect of stress relieving heat treatment of grade-1 Ti parts performed before and after forming on the surface finish and dimensional accuracy was studied. It was found that heat treatment at a temperature of 540 °C for 2 h improves the surface finish of formed parts resulting in a surface with little or no visible tool marks. Additionally, it improves the dimensional accuracy of parts after unclamping from the rig used for forming, in particular, that of parts with shallow wall angles (typically <25°). It was also noted that post-forming heat treatment improves the surface finish of parts. The surface topography of formed parts was studied using interferometry to yield areal surface roughness parameters and subsequently using secondary electron imaging. Back-scatter electron microscopy imaging results coupled with energy-dispersive X-ray (EDX) analysis showed that heat treatment prior to forming leads to tool wear as indicated by the presence of Fe in samples. Furthermore, post-forming heat treatment prevents curling up of formed parts due to compressive stresses if the formed part is trimmed

Challenges for IT-Enabled Formative Assessment of Complex 21st Century Skills

In this article, we identify and examine opportunities for formative assessment provided by information technologies (IT) and the challenges which these opportunities present. We address some of these challenges by examining key aspects of assessment processes that can be facilitated by IT: datafication of learning; feedback and scaffolding; peer assessment and peer feedback. We then consider how these processes may be applied in relation to the assessment of horizontal, general complex 21st century skills (21st CS), which are still proving challenging to incorporate into curricula as well as to assess. 21st CS such as creativity, complex problem solving, communication, collaboration and self-regulated learning contain complex constructs incorporating motivational and affective components. Our analysis has enabled us to make recommendations for policy, practice and further research. While there is currently much interest in and some progress towards the development of learning/assessment analytics for assessing 21st CS, the complexity of assessing such skills, together with the need to include affective aspects means that using IT-enabled techniques will need to be combined with more traditional methods of teacher assessment as well as peer assessment for some time to come. Therefore learners, teachers and school leaders must learn how to manage the greater variety of sorts and sources of feedback including resolving tensions of inconsistent feedback from different sources

The Proof of the Pudding: Examining Validity and Reliability of the Evaluation Framework for Learning Analytics

While learning analytics (LA) is maturing from being a trend to being part of the institutional toolbox, the need for more empirical evidences about the effects for LA on the actual stakeholders, i.e. learners and teachers, is increasing. Within this paper we report about a further evaluation iteration of the Evaluation Framework for Learning Analytics (EFLA) that provides an efficient and effective measure to get insights into the application of LA in educational institutes. For this empirical study we have thus developed and implemented several LA widgets into a MOOC platform’s dashboard and evaluated these widgets using the EFLA as well as the framework itself using principal component and reliability analysis. The results show that the EFLA is able to measure differences between widget versions. Furthermore, they indicate that the framework is highly reliable after slightly adapting its dimensions