A review, timeline, and categorization of learning design tools

Enabling teachers to define or portray efficient teaching ideas for sharing, reuse or adaptation has attracted the interest of Learning Design researchers and has led to the development of a variety of learning design tools. In this paper, we introduce a multi-dimensional framework for the analysis of learning design tools and use it to review twenty-nine tools currently available to researchers and practitioners. Lastly, we categorise these tools according to the main functionality that they offer

Developing a MovieBrowser for supporting analysis and browsing of movie content

There is a growing awareness of the importance of system evaluation directly with end-users in realistic environments, and as a result some novel applications have been deployed to the real world and evaluated in trial contexts. While this is certainly a desirable trend to relate a technical system to a real user-oriented perspective, most of these efforts do not involve end-user participation right from the start of the development, but only after deploying it. In this paper we describe our research in designing, deploying and assessing the impact of a web-based tool that incorporates multimedia techniques to support movie analysis and browsing for students of film studies. From the very start and throughout the development we utilize methodologies from usability engineering in order to feed in end-user needs and thus tailoring the underlying technical system to those needs. Starting by capturing real users’ current practices and matching them to the available technical elements of the system, we deployed an initial version of our system to University classes for a semester during which we obtained an extensive amount of rich usage data. We describe the process and some of the findings from this trial

Developing, deploying and assessing usage of a movie archive system among students of film studies

This paper describes our work in developing a movie browser application for students of Film Studies at our University. The aim of our work is to address the issues that arise when applying conventional user-centered design techniques from the usability engineering field to build a usable application when the system incorporates novel multimedia tools that could be potentially useful to the end-users but have not yet been practiced or deployed. We developed a web-based system that incorporates features as identified from the students and those features from our novel video analysis tools, including scene detection and classification. We deployed the system, monitored usage and gathered quantitative and qualitative data. Our findings show those expected patterns and highlighted issues that need to be further investigated in a novel application development. A mismatch between the users’ wishes at the interviews and their actual usage was noted. In general, students found most of the provided features were beneficial for their studies

Exploring the usage of a video application tool: Experiences in film studies

This paper explores our experiences in deploying a video application tool in film studies, and its evaluation in terms of realistic contextual end-users who have real tasks to perform in a real environment. We demonstrate our experiences and core lesson learnt in deploying our novel movie browser application with undergraduate and graduate students completing a Film Studies course in Dublin City University over a semester. We developed a system called MOVIEBROWSER2 that has two types of browsing modes: Advanced and Basic. In general, students found that the features we provided were beneficial for their studies. Some issues or mismatches arose during the trial. A ‘wish-list’ was drawn up that might be useful for the future system developer. The contribution and achievements reported in this article are on the demonstration and exploration of how advances in technology can be deployed, and media can be accessed in the context of a real user community. Exploring the usage indicates a positive acceptance among students, besides lessons learned that are important for further investigation

Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations

We examine if EEG-based cognitive load (CL) estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the nback~task. CL is estimated via two recent approaches: (a) Deep convolutional neural network, and (b) Proximal support vector machines. Experiments reveal that CL estimation suffers across visualizations motivating the need for effective machine learning techniques to benchmark visual interface usability for a given analytic task

Integrating social software into course design and tracking student engagement : early results and research perspectives

The uptake of social software is becoming more widespread in many sectors of education and organizational development. However, there is little empirical research on the impacts of adopting these technologies, and so it is difficult to determine appropriate pedagogic models and whether or not the desired learning outcomes are being realized. This paper reports early findings of an ongoing pilot study which is based on the concept of collaborative learning and supported by means of social software. It describes the educational philosophy behind the study and the teaching techniques used. The application of various features of social software, including blogs, file management and personalization, are discussed, as well as the different techniques for facilitating and measuring the level of student engagement with social software. The results indicate that student engagement with social software can be shaped by course design and activities that integrate educational technology into the course structure

Quality Indicators Guiding Secondary Career and Technical Education Programs of Study

The purpose of this study was to examine quality indicators currently guiding the rigor of secondary career and technical education (CTE) programs of study in the United States. Quality indicators are desirable characteristics or expectations for a comprehensive and effective CTE program of study. As of May 2017, we were able to locate publicly accessible secondary CTE quality program standards/guidelines for 38 states. A majority (n=24) updated their secondary CTE quality program standards/guidelines within the last five years (i.e., 2012-2017). Deductive content analysis was conducted to examine the 38 state profiles using the Association of Career and Technical Education (ACTE) Quality CTE Program of Study Framework 4.0 for coding purposes. Common quality elements and key quality indicators were identified from those state documents, which supplements the ACTE Framework. Implications and examples for practice are also discussed

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201