How to aggregate lesson observation data into learning analytics dataset?

The technological environment that supports the learning process tends to be the main data source for Learning Analytics. However, this trend leaves out those parts of the learning process that are not computer-mediated. To overcome this problem, involving additional data gathering techniques such as ambient sensors, audio and video recordings, or even observations could enrich datasets. This paper focuses on how the data extracted from the observations can be integrated with data coming from activity tracking, resulting in a multimodal dataset. The paper identifies the need for theoretical and pedagogical semantics in multimodal learning analytics, and examines the xAPI potential for the multimodal data gathering and aggregation. Finally, we propose an approach for pedagogy-driven observational data identification. As a proof of concept, we have applied the approach in two research works where observations had been used to enrich or triangulate the results obtained for traditional data sources. Through these examples, we illustrate some of the challenges that multimodal dataset may present when including observational data

Context-aware multimodal learning analytics taxonomy

Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges.Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges

Semantically annotated lesson observation data in learning analytics datasets: A reference model

Learning analytics (LA) and lesson observations are two approaches frequently used to study teaching and learning processes. In both cases, in order to extract meaningful data interpretations, there is a need for contextualization. Previous works propose to enrich LA datasets with observation data and to use the learning design as a framework to guide the data gathering and the later analysis. However, the majority of lesson observation tools collect data that is not compliant with LA datasets. Moreover, the connection between the learning design and the data gathered is not straightforward. This study reflects upon our research-based design towards an LA model for context-aware semantically annotated lesson observations that may be integrated in multimodal LA datasets. Six teachers (out of which 2 were also researchers) with previous experience in lesson observation were engaged in a focus group interview and participatory design session that helped us to evaluate the LA model through the conceptual design of Observata (a lesson observation tool that implements our model). The findings show the feasibility and usefulness of the proposal as well as the potential limitations in terms of adoption

Contextualising Learning Analytics with Classroom Observations: a Case Study

Educational processes take place in physical and digital places. To analyse educational processes, Learning Analytics (LA) enable data collection from the digital learning context. At the same time, to gain more insights, the LA data can be complemented with the data coming from physical spaces enabling Multimodal Learning Analytics (MMLA). To interpret this data, theoretical grounding or contextual information is needed. Learning designs (LDs) can be used for contextualisation, however, in authentic scenarios the availability of machine-readable LD is scarce. We argue that Classroom Observations (COs), traditionally used to understand educational processes taking place in physical space, can provide the missing context and complement the data from the colocated classrooms. This paper reports on a co-design case study from an authentic scenario that used CO to make sense of the digital traces. In this paper we posit that the development of MMLA approaches can benefit from codesign methodologies; through the involvement of the end-users (project managers) in the loop, we illustrate how these data sources can be systematically integrated and analysed to better understand the use of digital resources. Results indicate that CO can drive sense-making of LA data where predefined LD is not available. Furthermore, CO can support layered contextualisation depending on research design, rigour and systematic documentation/data collection efforts. Also, co-designing the MMLA solution with the end-users proved to be a useful approach

The socio-demographic patterning of sexual risk behaviour: a survey of young men in Finland and Estonia

<p>Abstract</p> <p>Background</p> <p>Sexually transmitted infections (STIs) among the youth are an increasing challenge for public health in Europe. This study provided estimates of men's (18–25 years) sexual risk behaviour and self-reported STIs and their socio-demographic patterning in Finland and Estonia; two countries that are geographically close, but have very different STI epidemics.</p> <p>Method</p> <p>Nationally representative cross-sectional population surveys with comparable survey questions were used. Data from self-administered questionnaires for 1765 men aged 18–25 years in Finland (85% of the age cohort was included in the sampling frame, 95% of the sample responded) and 748 in Estonia, with a response rate of 43% respectively, were analysed. Socio-demographic patterning of multiple partners, condom use and self-reported STIs are presented was studied using multiple logistic regression analysis.</p> <p>Results</p> <p>The main findings focus on associations found within each country. In Finland, higher age, low education and to a lesser extent relationship with a non-steady partner increased the likelihood of reporting multiple lifetime-partners, while in Estonia only higher age and low education revealed this effect. In relation to unprotected intercourse, in Finland, higher age, low education and relationship status with a steady partner increased the likelihood of reporting unprotected intercourse. In Estonia, the same was observed only for relationship status. In Finland the likelihood of self-reported STIs increased by older age and lower education and decreased by being with a non-steady partner, while in Estonia, a non-significant increase in self-reported STIs was observed only in the older age group.</p> <p>Conclusion</p> <p>A clear socio-demographic patterning for sexual behaviour and self-reported STIs was revealed in Finland, but a less consistent trend was seen in Estonia. The findings of this study suggest that prevention strategies should focus in Finland on less educated singles and in Estonia on young men generally.</p

HCI issues for web-based training course design

Use of Internet in education and training has been growing rapidly during last two years, training companies has recognized it's usefulness much faster than every other new kind of training before that. The short experience shows that WWW can be used in education and training in different ways and levels: from additional resource bank in face-to-face learning activities to carefully designed virtual classroom. In this paper we address design of Web-based Training (WBT), which we define as a form of distance education delivered via WWW.WorldWideWeb has all characteristics of an exellent distance learning environment: it's available everywhere, it has fast access to huge amounts of information and good communication tools. Designing and updating Web-based courseware is easy and not too expensive. It has caused "gold rush" within education and training community - many training providers are in hurry to set up on-line versions of their courses, without reasoning specific issues of Web-based course design. It's not enough to have just well-prepared content of the course or nice graphical design with streaming multimedia to make a good Web-based course.HCI design of Web-based courses is dependent on instructional design principles and decisions in one hand and on current technological capabilities of WWW environment in the other hand. In this paper we are going to open discussion on HCI issues of Web-based course design, based on the catalogue of WBT instructional tools. This catalogue has been prepared to support Web-based course design by presenting sets of available alternative solutions for every instructional event or functionality. At the end the paper is discussing the results of some Web-based courseware evaluation with respect of the catalogue implementation

Analyzing learning flows in digital learning ecosystems

This paper envisages emerging trends and methods in learning analytics for post-LMS era, where learning increasingly takes place in distributed, user-defined digital learning ecosystems. Inspired by the recent developments on uptake framework and Experience API, we propose learning flow as the main unit of analysis while studying learning-related interactions

E-textbooks: Towards the new socio-technical regime

This paper discusses the niche technologies that have and possibly will contribute to the future e-textbooks as a new socio-technical regime. We propose the conceptual map of textbook functionalities aiming at opening the conceptual discussion for brainstorming and finding scenarios how the niche technologies that explored novel textbook applications in learning might be best combined into the new \u201cartifact ecosystems\u201d regime. Jointly with workshop participants we aim to come up with metaphors and concepts depicting learning in this regime

Observing the use of e-textbooks in the classroom: Towards “offline” learning analytics

Learning analytics is an emerging approach that is equally popular among researchers and educators-practitioners. Although the methods and tools for LA have been developing fast, there still exist several unsolved problems: LA is too much data driven, weakly connected to theory and is able to analyse only the activities documented in an online setting - in LMS. We propose a solution for the LA unit of analysis drawing upon the research of existing practices and tools used for offline contexts: the data is coming from the physical learning interactions based on the observations in the classroom setting and captured with classroom observation application. We argue that if the unit of analysis has a particular logic and structure, it can unleash the possibilities for “offline” analytics that can be later integrated with online LA