scholarly journals Temporal Learning Analytics for Adaptive Assessment

2014 ◽  
Vol 1 (3) ◽  
pp. 165-168 ◽  
Author(s):  
Zacharoula Papamitsiou ◽  
Anastasios A. Economides
Keyword(s):  
Latent Variables ◽  
Learning Analytics ◽  
Research Work ◽  
Assessment System ◽  
Temporal Behaviour ◽  
Adaptive Assessment ◽  
Pls Analysis ◽  
Improved Learning ◽  
Future Work ◽  
Temporal Learning

Accurate and early predictions of students’ performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research work, we seek to identify and formalize temporal parameters as predictors of performance (“temporal learning analytics”-TLA) and examine students' temporal behaviour (i.e. in terms of time-spent) during testing. The goal is to specify a functional set of parameters that will be embedded in an adaptive assessment system in order to contribute to personalization of feedback services. We adopted the Partial Least-Squares (PLS) analysis method for formulating the causal dependencies between latent variables and the relations to their indicators. In this paper we present the motivation and rationale of our work, along with the followed methodology, initial results and contributions so far, and our plans on future work.

scholarly journals Critical Issues in Designing and Implementing Temporal Analytics

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Bodong Chen ◽  
Simon Knight ◽  
Alyssa Friend Wise
Keyword(s):  
Learning Analytics ◽  
Special Section ◽  
Temporal Data ◽  
Data Types ◽  
Temporal Features ◽  
Critical Issues ◽  
Potential Impact ◽  
Future Work ◽  
Temporal Learning ◽  
Learning Data

The importance of temporality in learning has been long established, but it is only recently that serious attention has begun to be paid to the precise identification, measurement, and analysis of the temporal features of learning. From 2009 to 2016, a series of temporality workshops explored temporal concepts and data types, analysis methods for exploiting temporal data, techniques for visualizing temporal information, and practical considerations for the use of temporal analyses in particular contexts of learning. Following from these efforts, this two-part Special Section serves to consolidate research working to progress conceptual, technical and practical tools for temporal analyses of learning data. In addition, in this second and final editorial, we aim to make four contributions to the ongoing dialogue around temporal learning analytics to help us move towards a clearer mapping of the research space. First, the editorial presents an overview of the five papers in Part 2 of the Special Section on Temporal Analyses, highlighting the dimensions of data types, learning constructs, analysis approaches, and potential impact. Second, it draws on the fluid relationship between ‘analyzed time’ and ‘experienced time’ to highlight the need for caution and criticality in the purposes temporal analyses are mobilized to serve. Third, it offers a guide for future work in this area by outlining important questions that all temporal analyses should intentionally address. Finally, it proposes next steps learning analytics researchers and practitioners can take collectively to advance work on the use of temporal analyses to support learning

Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques

2014 ◽  
pp. 101-130 ◽  
Author(s):  
Esraa El Hariri ◽  
Nashwa El-Bendary ◽  
Aboul Ella Hassanien ◽  
Amr Badr
Keyword(s):  
Feature Extraction ◽  
Fruits And Vegetables ◽  
Research Work ◽  
Assessment System ◽  
Assessment Process ◽  
Support Vector ◽  
Work Related ◽  
Color Features ◽  
Core Concepts

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production, since ripeness is the main quality indicator from the customers' perspective. To ensure optimum yield of high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based image classification approach to automate the ripeness assessment process of tomato via examining and classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent research work related to monitoring and classification of maturity stages for fruits/vegetables and provides the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of three phases, namely pre-processing, feature extraction, and classification phase. The classification process depends totally on color features (colored histogram and color moments), since the surface color of a tomato is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis (PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.

scholarly journals APPLYING INDUSTRY 4.0 AND EDUCATION 4.0 TO ENGINEERING EDUCATION

2019 ◽  
Author(s):  
Irina Neaga
Keyword(s):  
Higher Education ◽  
United Kingdom ◽  
Engineering Education ◽  
Learning Analytics ◽  
Industry 4.0 ◽  
Research Work ◽  
Undergraduate Level ◽  
Industrial Companies ◽  
Master Level ◽  
Smart Factories

This research work-in-progress deals with a holistic analysis of the impacts of Industry 4.0 (I4.0) for engineering education especially for University undergraduate (level 4-6), master (level 7) and PhD related manufacturing, automotive engineering and supply chain management programmes in United Kingdom higher education institutions. This analysis aims at providing support for further consolidated recommendations to enable the development of higher education engineering curriculum for enhancing I4.0 application for smart organisations and industrial companies within the digital supply chains. Also the paper provides an analysis of advancement from digitalisation in engineering education to the implementation of Education 4.0 and related practices of smart labs, and simulation of smart factories leading at the learning factory. A conceptual framework to support the application of big data and learning analytics in the School of Engineering from University of Wales Trinity St David, Swansea, United Kingdom has been identified, discussed and intended to apply in the context of applying learning analytics.

scholarly journals Time for Change: Why Learning Analytics Needs Temporal Analysis

2017 ◽  
Vol 4 (3) ◽  
Author(s):  
Simon Knight ◽  
Alyssa Friend Wise ◽  
Bodong Chen
Keyword(s):  
Learning Analytics ◽  
Temporal Analysis ◽  
Temporal Data ◽  
Research Practice ◽  
Process Data ◽  
Applied Learning ◽  
Privileged Access ◽  
Learning Research ◽  
Methodological Approaches ◽  
Temporal Learning

Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although “researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order” (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.

scholarly journals Infrared and Visible Image Fusion Techniques Based on Deep Learning: A Review

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2162
Author(s):  
Changqi Sun ◽  
Cong Zhang ◽  
Naixue Xiong
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Research Work ◽  
Image Features ◽  
Future Research ◽  
Learning Methods ◽  
Visible Image ◽  
Advantages And Disadvantages ◽  
Fusion Methods ◽  
Future Work

Infrared and visible image fusion technologies make full use of different image features obtained by different sensors, retain complementary information of the source images during the fusion process, and use redundant information to improve the credibility of the fusion image. In recent years, many researchers have used deep learning methods (DL) to explore the field of image fusion and found that applying DL has improved the time-consuming efficiency of the model and the fusion effect. However, DL includes many branches, and there is currently no detailed investigation of deep learning methods in image fusion. In this work, this survey reports on the development of image fusion algorithms based on deep learning in recent years. Specifically, this paper first conducts a detailed investigation on the fusion method of infrared and visible images based on deep learning, compares the existing fusion algorithms qualitatively and quantitatively with the existing fusion quality indicators, and discusses various fusions. The main contribution, advantages, and disadvantages of the algorithm. Finally, the research status of infrared and visible image fusion is summarized, and future work has prospected. This research can help us realize many image fusion methods in recent years and lay the foundation for future research work.

scholarly journals Computer Music research at FEEC/Unicamp: a snapshot of 2019

2019 ◽  
Author(s):  
Tiago Tavares ◽  
Bruno Masiero
Keyword(s):  
Computer Music ◽  
Research Work ◽  
Lessons Learned ◽  
Computer Engineering ◽  
The Past ◽  
The People ◽  
Music Research ◽  
State Of Affairs ◽  
The University ◽  
Future Work

This is a lab report paper about the state of affairs in the computer music research group at the School of Electrical and Computer Engineering of the University of Campinas (FEEC/Unicamp). This report discusses the people involved in the group, the efforts in teaching and the current research work performed. Last, it provides some discussions on the lessons learned from the past few years and some pointers for future work.

scholarly journals Perlustration on Recent Research into Multi Clouds integrated with Multi Party Computation

2019 ◽  
Vol 8 (6) ◽  
pp. 587-592
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Short Note ◽  
Research Work ◽  
Cloud Security ◽  
Ease Of Use ◽  
Technological Advancement ◽  
Future Work ◽  
Cloud Technologies ◽  
Multi Cloud

Recent advances in multi cloud technologies and multi-party computations have improved State of art usage of Cloud computing in real time scenarios. Primary reason behind using any service offered by others is ease of use with lesser economics. Cloud Computing is technological advancement which is in usage for last two decades because of its Pay-per-Usage policy offering enormous benefits across the user community. In spite of its enormous benefits, single factor which is stepping it back from its wider adoption throughout the digital society is its Security. Tremendous research work was done across industry and academia in association with cloud security. This paper focuses on brief history, real time deployment of cloud, usage, benefits, risks associated and Surveys various studies done by national and international organizations related to cloud security concerns and dwell upon the advantages of integrating multi clouds and multi-party computation techniques and emphasizes on recent research done across multi cloud environment and give a short note of future work to enhance security paradigm.

scholarly journals Learning analytics dashboard for twitter

2021 ◽  
Author(s):  
Anatoliy Gruzd ◽  
Nadia Conroy
Keyword(s):  
Learning Analytics ◽  
Online Survey ◽  
Qualitative Assessment ◽  
Named Entities ◽  
System Data ◽  
Twitter Users ◽  
The Individual ◽  
Intuitive Interface ◽  
Analytical Tools ◽  
Future Work

Designing a Learning Analytics Dashboard for Twitter-Facilitated Teaching Considering the increasing use of Twitter for both formal and informal learning, the primary goal of this project is to design a Learning Analytics (LA) dashboard to support instructors’ evaluation of Twitter-based teaching. To achieve this goal, we conducted an online survey involving 54 higher education instructors who have used Twitter in their past teaching. The main purpose was to identify why instructors use Twitter and what types of analytics they would consider valuable. The results of the survey evidence that instructors use Twitter to help students engage with class material, promote discussion, and build learning communities. Instructors expressed interest in analytical tools to help them quantitatively and qualitatively interpret Twitter data. Coupled with an in-depth literature review in this area, we relied on the survey data to prototype a Learning Analytics dashboard (https://dashboard.socialmediadata.org/educhat). Our online dashboard uses a simple, easy-to-read interface in accordance with previous successful dashboard implementations. Graphical visualizations allow instructors to monitor discussion patterns, such as the frequency and times of posting. Visual content breakdowns by number of retweets, original posts, and topics in the form of hashtags and named entities reveal the constituents of students’ posts. The dashboard provides additional analysis in the form of sentiment and subjectivity ranking as a way to contextually aid qualitative assessment. To support instructors’ awareness of class participation, we incorporated two visualizations that highlight the most active users and individuals who are most frequently mentioned in others’ tweets. Instructors can use the dashboard to gauge the participation at the individual- or classroom-level, and further discover what topics and links students discuss and share on Twitter. Three instructors piloted the LA dashboard over a 4-month semester in the Fall of 2017. Following their use, we conducted evaluation interviews with these instructors. Instructor evaluations confirmed that the proposed design is aligned with their pedagogical needs; they favored an intuitive interface that combined summative metrics for the entire class and personalized assessment of individual students. Based on instructors’ feedback, our future work will iteratively refine the design by integrating additional interactive features to adjust time scales of the output, investigate source data, collect data from lists of Twitter users (as opposed to a single hashtag), and further integrate the dashboard with other LMS (Learning Management System) data.

scholarly journals Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhonghua Zhang ◽  
Xifei Song ◽  
Lei Liu ◽  
Jie Yin ◽  
Yu Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Research Work ◽  
Three Dimensional ◽  
Large Data ◽  
Point Of View ◽  
Point System ◽  
Research Issues ◽  
Artificial Intelligence Technology ◽  
Point To Point ◽  
Future Work

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

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