Temporal Assessment of Self-Regulated Learning by Mining Students' Think-Aloud Protocols.

It has been widely theorized and empirically proven that self-regulated learning (SRL) is related to more desired learning outcomes, e.g., higher performance in transfer tests. Research has shifted to understanding the role of SRL during learning, such as the strategies and learning activities, learners employ and engage in the different SRL phases, which contribute to learning achievement. From a methodological perspective, measuring SRL using think-aloud data has been shown to be more insightful than self-report surveys as it helps better in determining the link between SRL activities and learning achievements. Educational process mining on the basis of think-aloud data enables a deeper understanding and more fine-grained analyses of SRL processes. Although students' SRL is highly contextualized, there are consistent findings of the link between SRL activities and learning outcomes pointing to some consistency of the processes that support learning. However, past studies have utilized differing approaches which make generalization of findings between studies investigating the unfolding of SRL processes during learning a challenge. In the present study with 29 university students, we measured SRL via concurrent think-aloud protocols in a pre-post design using a similar approach from a previous study in an online learning environment during a 45-min learning session, where students learned about three topics and wrote an essay. Results revealed significant learning gain and replication of links between SRL activities and transfer performance, similar to past research. Additionally, temporal structures of successful and less successful students indicated meaningful differences associated with both theoretical assumptions and past research findings. In conclusion, extending prior research by exploring SRL patterns in an online learning setting provides insights to the replicability of previous findings from online learning settings and new findings show that it is important not only to focus on the repertoire of SRL strategies but also on how and when they are used.

The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes.

The IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia.

Intelligent cognitive assistants support people who need help performing everyday tasks by detecting when problems occur and providing tailored and context-sensitive assistance. Spoken dialogue interfaces allow users to interact with intelligent cognitive assistants while focusing on the task at hand. In order to establish requirements for voice interfaces to intelligent cognitive assistants, we conducted three focus groups with people with dementia, carers, and older people without a diagnosis of dementia. Analysis of the focus group data showed that voice and interaction style should be chosen based on the preferences of the user, not those of the carer. For people with dementia, the intelligent cognitive assistant should act like a patient, encouraging guide, while for older people without dementia, assistance should be to the point and not patronising. The intelligent cognitive assistant should be able to adapt to cognitive decline.

Reducing disease burden and health inequalities arising from chronic disease among indigenous children: an early childhood caries intervention in Aotearoa/New Zealand.

BACKGROUND: Maaori are the Indigenous people of New Zealand and do not enjoy the same oral health status as the non-Indigenous majority. To overcome oral health disparities, the life course approach affords a valid foundation on which to develop a process that will contribute to the protection of the oral health of young infants. The key to this process is the support that could be provided to the parents or care givers of Maaori infants during the pregnancy of the mother and the early years of the child. This study seeks to determine whether implementing a kaupapa Maaori (Maaori philosophical viewpoint) in an early childhood caries (ECC) intervention reduces dental disease burden among Maaori children. The intervention consists of four approaches to prevent early childhood caries: dental care provided during pregnancy, fluoride varnish application to the teeth of children, motivational interviewing, and anticipatory guidance. METHODS/DESIGN: The participants are Maaori women who are expecting a child and who reside within the Maaori tribal area of Waikato-Tainui.This randomised-control trial will be undertaken utilising the principles of kaupapa Maaori research, which encompasses Maaori leadership, Maaori relationships, Maaori customary practices, etiquette and protocol. Participants will be monitored through clinical and self-reported information collected throughout the ECC intervention. Self-report information will be collected in a baseline questionnaire during pregnancy and when children are aged 24 and 36 months. Clinical oral health data will be collected during standardised examinations at ages 24 and 36 months by calibrated dental professionals. All participants receive the ECC intervention benefits, with the intervention delayed by 24 months for participants who are randomised to the control-delayed arm. DISCUSSION: The development and evaluation of oral health interventions may produce evidence that supports the application of the principles of kaupapa Maaori research in the research processes. This study will assess an ECC intervention which could provide a meaningful approach for Maaori for the protection and maintenance of oral health for Maaori children and their family, thus reducing oral health disparities. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12611000111976.

The NITE XML Toolkit: flexible annotation for multimodal language data.

Multimodal corpora that show humans interacting via language are now relatively easy to collect. Current tools allow one either to apply sets of time-stamped codes to the data and consider their timing and sequencing or to describe some specific linguistic structure that is present in the data, built over the top of some form of transcription. To further our understanding of human communication, the research community needs code sets with both timings and structure, designed flexibly to address the research questions at hand. The NITE XML Toolkit offers library support that software developers can call upon when writing tools for such code sets and, thus, enables richer analyses than have previously been possible. It includes data handling, a query language containing both structural and temporal constructs, components that can be used to build graphical interfaces, sample programs that demonstrate how to use the libraries, a tool for running queries, and an experimental engine that builds interfaces on the basis of declarative specifications.