Adaptive and intelligent mentoring to increase user attentiveness in learning activities

In the past decades intelligent mentoring systems have rapidly increased. In e-learning environment there has been an exponential growth in technological development environments and number of users that are addressed, hence an intelligent mentoring system should capture the user’s attention in order to improve results when focused in (e)learning tasks (i.e. serve both as a support of presence lessons and for distance form of studies – e-learning). It is important to note that the process of teaching-learning requires an interaction between the different actors involved: the tutor, the student, the expert domain and the learning environment or interface. In this paper we propose an innovative approach of an intelligent mentoring system that monitors the user’s biometric behaviour and measures his/her attention level during e-learning activities. Additionally, a machine learning categorisation model is presented that monitors students’ activity during school lessons. Nowadays computers are used as important working tools in many places, where we intend to use non-invasive methods of intelligent orientation through the observation of the user’s interaction with the computer.This work has been supported by: SENESCYT - Universidad do Minho and Secretaría de Educación Superior, Ciencia, Tecnología e Innovación within the Project: SENESCYT-SDFC-DSEFC-2017-2855-O; Part-funded by ERDF European Regional Development Fund and by National Funds through the FCT Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. The work of Filipe Gonçalves is supported by a FCT grant with the reference ICVS-BI-2016-005

Semport: a personalized semantic portal.

This paper presents an ontology-based semantic portal, SEMPort, which aims to support both content providers and the users of the portal during providing information, browsing and searching. The content is enriched with context-based semantic hyperlinks and personalized views. Distributed content editing/provision is supplied for the maintenance of the contents in real-time. As a case study, SEMPort is tested on the school's Course Modules Web Page (CMWP) and evaluated using this domain

Data-Driven Personalization of Student Learning Support in Higher Education

Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data – one that puts the relationship between teacher and student at the center. Our approach means that teachers’ pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation

Model-based Cross-correlation Search for Gravitational Waves from the Low-mass X-Ray Binary Scorpius X-1 in LIGO O3 Data

We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO and Advanced Virgo. This is a semicoherent search that uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25 to 1600 Hz, as well as ranges in orbital speed, frequency, and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100 and 200 Hz, correspond to an amplitude h0 of about 10−25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4 × 10−26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically marginalized upper limits are close to the predicted amplitude from about 70 to 100 Hz; the limits assuming that the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40 to 200 Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500 Hz or more