Basics of qualitative research: Grounded theory procedures and techniques

The power of feedback.

An Introduction to MultiAgent Systems.

Organizational learning II: Theory, method and practice

http://webuser.unicas.it/dweb/gestione/download.php?id=1432

Mass customization: Literature review and research directions

With growing reliance on modeling in product development, it is imperative to describe product families in a cohesive way. In particular, mass customization calls for a close integration of product life cycle from customer recognition to delivery and services. This paper proposes a triple-view scheme for modeling product families. Technical challenges are discussed by comparing product family modeling with modeling single products. Individual modeling formalisms for different views are discussed. An example of product family modeling in power supply design is presented to illustrate the feasibility and potential of the proposed approach.

Product family modeling for mass customization

Online learners participate in various educational activities including reading, writing, watching video tutorials, online exams, and online meetings. During the participation in these educational activities, they show various engagement levels, such as boredom, frustration, delight, neutral, confusion, and learning gain. To provide personalized pedagogical support through interventions to online learners, it is important for online educators to detect their online learners’ engagement status precisely and efficiently. This paper presents a review of the state of the art in engagement detection in the context of online learning. We classify the existing methods into three main categories—automatic, semi-automatic and manual—considering the methods’ dependencies on learners’ participation. Methods in each category are then divided into subcategories based on the data types (e.g., audio, video, texts for learner log data etc.) they process for the engagement detection. In particular, the computer vision based methods in the automatic category that use facial expressions are examined in more details because they are found to be promising in the online learning environment. These methods are nonintrusive in nature, and the hardware and the software that these methods use to capture and analyze video data are cost-effective and easily achievable. Different techniques in the field of computer vision and machine learning are applied in these methods for the engagement detection. We then identify their challenges of engagement detection and explore available datasets and performance metrics for engagement detection, and provide recommendations for the future to advance the technology of engagement detection for online education.

/pdf/engagement-detection-in-online-learning-a-review-1yswzz8nht.pdf

Engagement detection in online learning: a review

Developing virtual environments for industrial training

Writing is a core skill that learners are expected to develop in their early school years and use effectively throughout their later school years. Historically, writing has been considered the purview of grade school education, yet there is evidence that learners seem to lack basic writing skills even at the university level. Unfortunately, the challenges posed by the volume of data created when students write have hampered writing researchers’ attempts to study the impacts of grade school writing initiatives in depth. This chapter introduces two novel approaches to academic writing activities that hold the potential to enhance writing competence and make it easier for researchers to understand the impact of writing interventions. The first uses mobile devices in a situated learning context, and the second uses a mixed-initiative writing system in the classroom.

Fuhua Lin

Papers

Product family modeling for mass customization

Engagement detection in online learning: a review

Developing virtual environments for industrial training

Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers

Deep Learning for Medical Image Processing