The power of feedback.

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Cloud is, using the main characteristics typically associated with this paradigm in the literature. More than 20 definitions have been studied allowing for the extraction of a consensus definition as well as a minimum definition containing the essential characteristics. This paper pays much attention to the Grid paradigm, as it is often confused with Cloud technologies. We also describe the relationships and distinctions between the Grid and Cloud approaches.

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A break in the clouds: towards a cloud definition

On Educational psychology: A cognitive view.

Data-driven Soft Sensors in the process industry

e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments

In this paper we attempt to measure the semantic distance between forum dialogues in five MOOCs of different subject matters (humanities, mathematics and computing). We examine if such distance can provide evidence of transferable linguistic features, suggesting transferability of forum classification models. For this purpose, we employed Latent Dirichlet Allocation in order to extract the main discussion topics for each MOOC and proceeded to a pairwise semantic comparison between them. We focused on three categories of forum topics, i.e., Content-Related, Logistics-Related and Social Building. In the Content-Related discussions, the study confirmed semantic distance between the humanities course and the other four, while the distances among the mathematics and computing courses were shorter than expected. On the other hand, Logistics-Related and Social Building topics contained a more transferable set of features. The approach that we followed provides insights that could help MOOC researchers interpret the transferable features of their available data to employ in their classification tasks.

Exploring the "distance" between MOOC forums: A comparative study on discussion topics

The adoption of LA proposals in everyday learning and teaching practice is still slow, and requires effective identification and communication between different stakeholder communities (including researchers, teachers, students and technology developers). To complement high-level institutional, policy-oriented frameworks to promote LA adoption, this workshop proposes to look at how LA innovations impact, or are conditioned by, everyday practice at the classroom level (what some authors call “classroom orchestration”). In this half-day collaborative knowledge building event, participants from these different stakeholder communities bring examples of LA adoption efforts, discuss them through the lens of such classroom orchestration, and further develop frameworks and guidance on what issues should be effectively discussed (and how this communication can be supported).

Orchestrating learning analytics: Learning analytics adoption at the classroom level

Lack of timely instructors’ support when the learners are struggling with the course contents and activities is one frequent problem of MOOC learners. The early identification of these learners could help instructors spend part of their limited time assisting them and avoid potential dropouts. This paper presents a MOOC case study that explores the behavior of learners who reported problems in private messages and discussion forums. The study aimed at the identification of parameters that might allow the detection of learners struggling with different course aspects. As the results suggested, the comparison of the learners’ activity traces reveals some common sequences that in the future could facilitate the identification of learners facing problems, even without reporting them. On the other hand, statistical analyses on learners’ behavior showed non-significant differences between the learners reporting putting their maximum effort to overcome a problem before asking for help and the ones who did not.

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“Error 404- Struggling Learners Not Found” Exploring the Behavior of MOOC Learners

Background Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives This paper focuses on instructor-led feedback mediated by LA tools in MOOCs. Our goal is to answer how, to what extent data-driven feedback is provided to learners, and what its impact is. Methods We conducted a systematic literature review on the state-of-the-art LA-informed instructor-led feedback in MOOCs. From a pool of 227 publications, we selected 38 articles that address the topic of LA-informed feedback in MOOCs mediated by instructors. We applied etic content analysis to the collected data. Results and Conclusions The results revealed a lack of empirical studies exploring LA to deliver feedback, and limited attention on pedagogy to inform feedback practices. Our findings suggest the need for systematization and evaluation of feedback. Additionally, there is a need for conceptual tools to guide instructors' in the design of LA-based feedback. Takeaways We point out the need for systematization and evaluation of feedback. We envision that this research can support the design of LA-based feedback, thus contributing to bridge the gap between pedagogy and data-driven practice in MOOCs. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcal.12799

Yannis Dimitriadis

Papers

e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments

Exploring the "distance" between MOOC forums: A comparative study on discussion topics

Orchestrating learning analytics: Learning analytics adoption at the classroom level

“Error 404- Struggling Learners Not Found” Exploring the Behavior of MOOC Learners

Delving into instructor‐led feedback interventions informed by learning analytics in massive open online courses