Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

Açıköğretim ile 40 Yıl: Uygulamalar ve Araştırmalar

It is a great pleasure to celebrate the 40th anniversary of Anadolu University Open Education System. On this special occasion, we are honoured to write a foreword to this book prepared with great pride and excitement. Founded 40 years ago, Anadolu University Open Education System took a revolutionary step in the field of higher education in our country. This system has increased accessibility to educational opportunities and ensured that everyone has the opportunity to learn equally and fairly. This unique and innovative approach has enabled thousands of students to turn their dreams into reality, and today it has turned into a great success story with millions of graduates. This book invites its readers on a journey that reflects the 40-year experience of Anadolu University Open Education System. It shares the work of the units, managers, academicians and employees who have achieved countless successes in the process from the establishment of the system until today. At the same time, it touches upon the difficulties encountered in this process, the labour and effort behind the successes, and the innovations and developments in the system. In this sense, the 40-year journey of the open education system is covered in this book through scientific research studies, the narration of the historical witnesses of the development and the evaluation of the practices, and the presentation of suggestions for the development of the system. Accordingly, the book consists of two parts. In the first part, the description of the important components of the system such as applications, learning environments, activities of the units, quality, learner evaluation, student services, etc. carried out in Anadolu University Open Education System are presented within the historical development and the points they have reached today. In the second part, researches on the evaluation and development of the open education system are presented. While preparing this book, we have seen how Anadolu University Open Education System has grown with its creativity, belief in learning and search for continuous development. At the same time, we have closely witnessed the opportunities it offers to its students and the transformation in their lives. This book will be an important resource for shaping our vision for the future as well as evaluating our past. We would like to take this opportunity to congratulate Anadolu University Open Education System on its 40th anniversary, sincerely congratulate everyone who has contributed to this success, and thank all the authors who have contributed to this book. As the editors, we hope that the book will be a tool to celebrate our past, share our achievements and inspire the future

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data