The role of learning design and learning analytics in MOOCs

The last couple of years there has been a lot of attention for MOOCs. More and more universities start offering MOOCs. Although the open dimension of MOOC indicates that it is open in every aspect, in most cases it is a course with a structure and a timeline within which learning activities are positioned. There is a contradiction there. The open aspect puts MOOCs more in the non-formal professional learning domain, while the course structure takes it into the formal, traditional education domain. Accordingly, there is no consensus yet on solid pedagogical approaches for MOOCs. Something similar can be said for learning analytics, another upcoming concept that is receiving a lot of attention. Given its nature, learning analytics offers a large potential to support learners in particular in MOOCs. Learning analytics should then be applied to assist the learners and teachers in understanding the learning process and could predict learning, provide opportunities for pro-active feedback, but should also results in interventions aimed at improving progress. This paper illustrates pedagogical and learning analytics approaches based on practices developed in formal online and distance teaching university education that have been fine-tuned for MOOCs and have been piloted in the context of the EU-funded MOOC projects ECO (Elearning, Communication, Open-Data: http://ecolearning.eu) and EMMA (European Multiple MOOC Aggregator: http://platform.europeanmoocs.eu).

Learning design guided learning analytics in MOOCs

Poster presentation for our paper Brouns, F., & Firssova, O. (2016, October).The role of learning design and learning analytics in MOOCs. Paper presented at 9th EDEN Research Workshop, Oldenburg, Germany.

Applying standards to systematize learning analytics in serious games

Learning Analytics is an emerging field focused on analyzing learners’ interactions with educational content. One of the key open issues in learning analytics is the standardization of the data collected. This is a particularly challenging issue in serious games, which generate a diverse range of data. This paper reviews the current state of learning analytics, data standards and serious games, studying how serious games are tracking the interactions from their players and the metrics that can be distilled from them. Based on this review, we propose an interaction model that establishes a basis for applying Learning Analytics into serious games. This paper then analyzes the current standards and specifications used in the field. Finally, it presents an implementation of the model with one of the most promising specifications: Experience API (xAPI). The Experience API relies on Communities of Practice developing profiles that cover different use cases in specific domains. This paper presents the Serious Games xAPI Profile: a profile developed to align with the most common use cases in the serious games domain. The profile is applied to a case study (a demo game), which explores the technical practicalities of standardizing data acquisition in serious games. In summary, the paper presents a new interaction model to track serious games and their implementation with the xAPI specification.

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.

Breast cancer outcomes following predictive BRCA testing in Northern Ireland

Objectives: Since 1995, BRCA testing has identified 445 women in Northern Ireland who carry a pathogenic BRCA1/2 mutation, without breast cancer (bca) at testing. This study examined outcomes with reference to management, bca risk, and incidence following positive predictive testing. Methods: Patients were identified from the regional genetics database. Electronic clinical records were used to obtain management and outcome details. Median follow-up was to bca diagnosis, risk-reducing mastectomy (rrm), death, or last follow-up. Results: 169 women had a BRCA1 mutation, and 276 BRCA2. ■ BRCA1 cohort: Median follow-up post-testing was 3 years. 56 Women (33%) had rrm, and 12 are awaiting rrm (total 68, 40%) at a median age of 36 years. 12 Women (7%) developed bca, at a median of 2 years following testing. 4 Women were diagnosed with bcas incidentally at rrm. 7 Patients had bilateral mastectomies following a cancer diagnosis. 1 Woman developed bca following rrm (1.7%). Three deaths were reported: 1 breast cancer (1.7%), 1 ovarian cancer (1.7%), and 1 with no recorded breast/ovarian cancer diagnosis. ■ BRCA2 cohort: Median follow-up post-testing was 6 years. rrm was carried out in 75 women (27%), with 20 awaiting rrm (total 95, 35%); median age: 39 years. 16 Women developed bca (5.8%), at a median of 5 years from testing. 6 Women were diagnosed with cancer incidentally at rrm; 9 women had bilateral mastectomy following diagnosis, and 1 developed bca following rrm (1.3%). Five deaths were reported: 1 bca, 1 ovarian cancer, and 3 with no recorded breast/ovarian cancer diagnosis. Conclusions: The uptake of rrm following predictive BRCA testing in Northern Ireland is comparable with that reported elsewhere. The incidence of bca following rrm is low (<2%) in our cohort, with low breast and ovarian cancer–specific mortality following positive predictive testing.

Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance.

The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution.

Searching for Predictive Subgroups

Thesis (Ph.D.)--University of Washington, 2016-08

Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields.

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

A predictive computational fluid dynamics study of carotid artery

Abstract not available

Temperature regulation of a pilot-scale batch reaction system via explicit model predictive control

In this paper, the temperature of a pilot-scale batch reaction system is modeled towards the design of a controller based on the explicit model predictive control (EMPC) strategy -- Some mathematical models are developed from experimental data to describe the system behavior -- The simplest, yet reliable, model obtained is a (1,1,1)-order ARX polynomial model for which the mentioned EMPC controller has been designed -- The resultant controller has a reduced mathematical complexity and, according to the successful results obtained in simulations, will be used directly on the real control system in a next stage of the entire experimental framework