This document discusses using analytics and data to gain actionable insights from open online courses. It outlines how data can be collected from various sources like YouTube, Google Analytics, and Canvas APIs to analyze student engagement patterns using metrics like social networks, discourse, and content. The goal is to detect subpopulations and understand factors like isolation, group dynamics, and information brokers to improve the learning experience.
Predicting Current User Intent with Contextual Markov ModelsJulia Kiseleva
Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context
discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us
to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of
user intentions with contextual Markov models.
The talk at Twente University on 28 July 2014 Julia Kiseleva
Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
Scalable Learning Analytics and Interoperability – an assessment of potential...LACE Project
A presentation given at the 2015 EUNIS Congress, held at Abertay University in Dundee, June 2015.
Learning analytics is now moving from being a research interest to topic for adoption. As this happens, the challenge of efficiently and reliably moving data between systems becomes of vital practical importance. In this context, “scalable learning analytics” is not intended to refer to infrastructural throughput, but to refer to the feasibility of a combination of: a) pervasive system
integration, and b) efficient analytical and data management practices. There are a number of
considerations that are of particular relevance to learning analytics in addition to elements that are generic to analytics. This contribution to EUNIS 2015 seeks to clarify, by argument and through evidence, both where there are potential benefits and limitations to applying interoperability specifications (and standards) in the service of scalable learning analytics.
Candace Thille: The Science of Learning, Big Data, Technology, and Transfor...Alexandra M. Pickett
Will technology change the way we teach and learn? Join Professor Thille for an engaging discussion on technology and the science of learning. She’ll share what we’ve learned from open online courses and what this means for higher education.
Exploring Choice Overload in Related-Article Recommendations in Digital Libra...Joeran Beel
Citation:
Beierle, Felix, Akiko Aizawa, and Joeran Beel. “Exploring Choice Overload in Related-Article Recommendations in Digital Libraries.” In 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017.
Abstract: We investigate the problem of choice overload – the difficulty of making a decision when faced with many options – when displaying related-article recommendations in digital libraries. So far, research regarding to how many items should be displayed has mostly been done in the fields of media recommendations and search engines. We analyze the number of recommendations in current digital libraries. When browsing fullscreen with a laptop or desktop PC, all display a fixed number of recommendations. 72% display three, four, or five recommendations, none display more than ten. We provide results from an empirical evaluation conducted with GESIS ’ digital library Sowiport, with recommen dations delivered by recommendations-as-a-service provider Mr. DLib.
We use click-through rate as a measure of recommendation effectiveness based on 3.4 million delivered recommendations. Our results show lower click-through rates for higher numbers of recommendations and twice as many clicked recommendations when displaying ten instead of one related-articles. Our results indicate that users might quickly feel overloaded by choice.
Predicting Current User Intent with Contextual Markov ModelsJulia Kiseleva
Abstract—In many web information systems like e-shops and information portals predictive modeling is used to understand user intentions based on their browsing behavior. User behavior is inherently sensitive to various contexts. Identifying such relevant contexts can help to improve the prediction performance. In this work, we propose a formal approach in which the context
discovery process is defined as an optimization problem. For simplicity we assume a concrete yet generic scenario in which context is considered to be a secondary label of an instance that is either known from the available contextual attribute (e.g. user location) or can be induced from the training data (e.g. novice vs. expert user). In an ideal case, the objective function of the optimization problem has an analytical form enabling us
to design a context discovery algorithm solving the optimization problem directly. An example with Markov models, a typical approach for modeling user browsing behavior, shows that the derived analytical form of the optimization problem provides us with useful mathematical insights of the problem. Experiments with a real-world use-case show that we can discover useful contexts allowing us to significantly improve the prediction of
user intentions with contextual Markov models.
The talk at Twente University on 28 July 2014 Julia Kiseleva
Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
Scalable Learning Analytics and Interoperability – an assessment of potential...LACE Project
A presentation given at the 2015 EUNIS Congress, held at Abertay University in Dundee, June 2015.
Learning analytics is now moving from being a research interest to topic for adoption. As this happens, the challenge of efficiently and reliably moving data between systems becomes of vital practical importance. In this context, “scalable learning analytics” is not intended to refer to infrastructural throughput, but to refer to the feasibility of a combination of: a) pervasive system
integration, and b) efficient analytical and data management practices. There are a number of
considerations that are of particular relevance to learning analytics in addition to elements that are generic to analytics. This contribution to EUNIS 2015 seeks to clarify, by argument and through evidence, both where there are potential benefits and limitations to applying interoperability specifications (and standards) in the service of scalable learning analytics.
Candace Thille: The Science of Learning, Big Data, Technology, and Transfor...Alexandra M. Pickett
Will technology change the way we teach and learn? Join Professor Thille for an engaging discussion on technology and the science of learning. She’ll share what we’ve learned from open online courses and what this means for higher education.
Exploring Choice Overload in Related-Article Recommendations in Digital Libra...Joeran Beel
Citation:
Beierle, Felix, Akiko Aizawa, and Joeran Beel. “Exploring Choice Overload in Related-Article Recommendations in Digital Libraries.” In 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017.
Abstract: We investigate the problem of choice overload – the difficulty of making a decision when faced with many options – when displaying related-article recommendations in digital libraries. So far, research regarding to how many items should be displayed has mostly been done in the fields of media recommendations and search engines. We analyze the number of recommendations in current digital libraries. When browsing fullscreen with a laptop or desktop PC, all display a fixed number of recommendations. 72% display three, four, or five recommendations, none display more than ten. We provide results from an empirical evaluation conducted with GESIS ’ digital library Sowiport, with recommen dations delivered by recommendations-as-a-service provider Mr. DLib.
We use click-through rate as a measure of recommendation effectiveness based on 3.4 million delivered recommendations. Our results show lower click-through rates for higher numbers of recommendations and twice as many clicked recommendations when displaying ten instead of one related-articles. Our results indicate that users might quickly feel overloaded by choice.
Slides for a discussion on a brief Nature comment on Bioinformatics Cores and an older Plos One perspective that covers suggested best practices for Bioinformatics Cores.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Slides presented at the 5th International Meeting of OERu partners, including some contributions from the floor on research priorities in open education
DESCRIPTION:
Learning analytics is at a critical juncture in its lifecycle. To date, much of the learning analytics-related research, software development, and standards work that exists has taken place in relative isolation. This lack of collaboration, openness, and integrated systems greatly limits the
potential of learning analytics. LA initiatives have typically been dependent upon “closed” systems, proprietary data models and single use tools – as opposed to an integrated software suite for analyzing and communicating data on learning processes.
As institutions begin to move past discussion and into implementation of learning analytics environments, the realization of an open source platform for learning analytics becomes increasingly important as an option for institutions to consider alongside commercial offerings. In this presentation, learning analytics practitioners Josh Baron, Sandeep Jayaprakash and Alan Berg discuss a strategic vision of an open source platform, including standards, systems, and tools, that can lower the barrier to entry for institutions looking to get started with learning analytics.
There will be a short demo of current components of the platform as well as details on accessing/contributing to the open-source code repository and how to get more involved in the Apereo LAI.
Let’s get there! Towards policy for adoption of learning analyticsDragan Gasevic
The field learning analytics is established with the promise for the education sector to embrace the use of data for decision making. There are many examples of successful use of learning analytics to enhance student experience, increase learning outcomes, and optimize learning environments. Despite much interest in learning analytics, many higher education institutions are still looking for effective ways that can enable systemic uptake. The talk will first describe some selected examples of the successful use of learning analytics in higher education. Key challenges identified to affect implementation of learning analytics will then be discussed. This will be followed with an overview of an approach to the development of institutional policy and strategy for the learning analytics implementation in higher education. The talk will be based on the findings of several international studies and will critically interrogate the role of institutional and cultural differences.
Overview of PowerAnalyzer 4.0
Schema definition - Analytics Design
Creating a Report
Working with Report Data
Working with the Dashboards
Administrating PowerAnalyzer
Today’s organizations face more sophisticated competition than any other instance of time. The pressures of competition, profitability, customer retention and efficiency have increased multifold. Sustainable business performance is being driven faster decision making capacity by finding meaning out of high volume and velocity of real-time structured and unstructured data in different formats.
Intro to Data Science for Enterprise Big DataPaco Nathan
If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Deriving value from analytics requires much more than purchasing technology. University of Kentucky's analytics journey utilized fostering a bottom-up emergent community of practice as well as top-down organizational maneuvers. This presentation shares different aspects of the University of Kentucky score.
Slides for a discussion on a brief Nature comment on Bioinformatics Cores and an older Plos One perspective that covers suggested best practices for Bioinformatics Cores.
A Pulse of Predictive Analytics In Higher Education │ Civitas LearningCivitas Learning
Civitas Learning presents the findings of our survey conducted during the September 2014 Civitas Learning Summit, where more than 100 leaders representing 40 Pioneer Partner institutions gathered to share more on their work. The survey, distributed to all participants, resulted in 74 responses highlighting how this cross-section of higher education institutions are using advanced analytics to power student success initiatives.
Slides presented at the 5th International Meeting of OERu partners, including some contributions from the floor on research priorities in open education
DESCRIPTION:
Learning analytics is at a critical juncture in its lifecycle. To date, much of the learning analytics-related research, software development, and standards work that exists has taken place in relative isolation. This lack of collaboration, openness, and integrated systems greatly limits the
potential of learning analytics. LA initiatives have typically been dependent upon “closed” systems, proprietary data models and single use tools – as opposed to an integrated software suite for analyzing and communicating data on learning processes.
As institutions begin to move past discussion and into implementation of learning analytics environments, the realization of an open source platform for learning analytics becomes increasingly important as an option for institutions to consider alongside commercial offerings. In this presentation, learning analytics practitioners Josh Baron, Sandeep Jayaprakash and Alan Berg discuss a strategic vision of an open source platform, including standards, systems, and tools, that can lower the barrier to entry for institutions looking to get started with learning analytics.
There will be a short demo of current components of the platform as well as details on accessing/contributing to the open-source code repository and how to get more involved in the Apereo LAI.
Let’s get there! Towards policy for adoption of learning analyticsDragan Gasevic
The field learning analytics is established with the promise for the education sector to embrace the use of data for decision making. There are many examples of successful use of learning analytics to enhance student experience, increase learning outcomes, and optimize learning environments. Despite much interest in learning analytics, many higher education institutions are still looking for effective ways that can enable systemic uptake. The talk will first describe some selected examples of the successful use of learning analytics in higher education. Key challenges identified to affect implementation of learning analytics will then be discussed. This will be followed with an overview of an approach to the development of institutional policy and strategy for the learning analytics implementation in higher education. The talk will be based on the findings of several international studies and will critically interrogate the role of institutional and cultural differences.
Overview of PowerAnalyzer 4.0
Schema definition - Analytics Design
Creating a Report
Working with Report Data
Working with the Dashboards
Administrating PowerAnalyzer
Today’s organizations face more sophisticated competition than any other instance of time. The pressures of competition, profitability, customer retention and efficiency have increased multifold. Sustainable business performance is being driven faster decision making capacity by finding meaning out of high volume and velocity of real-time structured and unstructured data in different formats.
Intro to Data Science for Enterprise Big DataPaco Nathan
If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Deriving value from analytics requires much more than purchasing technology. University of Kentucky's analytics journey utilized fostering a bottom-up emergent community of practice as well as top-down organizational maneuvers. This presentation shares different aspects of the University of Kentucky score.
SMART Infrastructure Facility was pleased to host Dr Ruth Deakin Crick, a Reader in Systems Learning and Leadership, at University of Bristol, UK as she presented ‘Learning Journeys: making learning visible in developing infrastructure futures’ as part of the SMART Seminar Series on October 16th, 2014.
JISC RSC London Workshop - Learner analyticsJames Ballard
Introduction to learning analytics and approaches to learner engagement to raise awareness and set the seen for upcoming projects and advice for supported learning providers.
Theorizing data, information and knowledge constructs and their inter-relatio...Cranfield University
Good explanatory constructs for Data, Information and Knowledge are central to the Information Systems (IS) field in general, and in particular to theorising how best to generate insight from Data. The central role of Knowledge within such theory has been highlighted recently, as well as the importance of Learning and Research frames (for Data Analytics). Building on these ideas, this paper briefly reviews several related literatures, for relevant ideas to enrich IS theory building. A consensus is found as to the complex, socially constructed nature of Knowledge or Knowing, and the importance of human sensemaking for theorizing how new insight is generated. The paper argues for an intuitive conceptual and practical distinction between Data (which exists as an independent, reified resource), and Information and Knowledge (both of which are embodied or embrained). It briefly outlines how the ideas identified can contribute to theorizing, highlighting specific areas for further inter-disciplinary research.
Information Literacy meets Employabilitydbslibrary
The proficiencies learned through information literacy (IL) training are life long skills that can be employed post graduation, especially in relation to employment. This presentation examines the evolution of IL; from traditional IL to digital IL in the workplace. The presentation seeks to highlight the theories and proficiencies of workplace IL, the attributes associated with employability and finishes by describing how Dublin Business School's information literacy programme has recently expanded by launching a new class "Information Skills for Interview Preparation".
Confronting Reality with Big Data & Learning Analytics
We are experiencing an explosion in the quantity of data available online from archives and live streams. Learning Analytics is concerned with how educational research, and learning platform design, can make more effective use of such data (Long & Siemens, 2011). Improving outcomes through the analysis of data is of interest to researchers, administrators, systems architects, social media developers, educators and learners. Analytics are being held up by some as a way to confront, and tackle, the tough new realities of less money, less attention, and higher accountability for quality of learning.
Researchers and vendors are building reporting capabilities into tools that provide unprecedented levels of data on learners. This symposium will show what is possible, and what's coming soon. What objections could possibly be raised to such progress?
However, information infrastructure embodies and shapes worldviews: classification schemes are not only systematic ways to capture and preserve, but also to forget, by virtue of what remains invisible (Bowker & Star, 1999). Learning analytics and recommendation engines are designed with a particular conception of ‘success’, driving the patterns deemed to be evidence of progress, the interventions that are deemed appropriate, the data captured and the rules that fire in software.
This symposium will air some of the critical arguments around the limits of decontextualised data and automated analytics, which often appear reductionist in nature, failing to illuminate higher order learning. There are complex ethical issues around data fusion, and it is not clear to what extent learners are empowered, in contrast to being merely the objects of tracking technology. Educators may also find themselves at the receiving end of a new battery of institutional ‘performance indicators’ that do not reflect what they consider to be authentic learning and teaching.
This Symposium will provide the opportunity to hear a series of brief presentations introducing contrasting perspectives, before the debate is opened to all. Speakers from a cross-section of The Open University will describe how we are connecting datasets, analysing student data and prototyping next generation analytics. Complementing this, JISC will present a national capability perspective, with an update on the JISC CETIS ‘landscape analysis’ of the field, which will clarify potential benefits, issues to consider, and help institutions to assess their current capability and possible next steps.
Participants will catch up with developments in this fast moving field, through exposure to the possibilities of analytics, as well as issues to be alert to.
Twitter in Education: Interactively exploring the conversation with TAGS and ...Martin Hawksey
There has been much research in the use of social media to support learning and teaching. In many instances it is argued that it enables a decentralization of learning moving towards a distributed model which has many benefits including supporting a stronger foundation for lifelong learning.
Twitter is one service that has been widely used within this context. The introduction of hashtags as a mechanism to allow communities to form and contribute to a topic is now a well established model within both formal and informal education as well as in society in general. The use of Twitter in this way removes boundaries extending the opportunities for co-learning, in particular, discussions can become less siloed, every contribution to a hashtag community is potentially another opportunity for someone else to join the conversation. The thinning of the walls in this way is not without it implications and the vulnerability of being a learner should never be underestimated. Another consideration is that Twitter has been adopted as a tool to support learning in this way rather than being designed for this purpose. As a result exploring and finding understanding within hashtag communities can be problematic and with many open learning contexts individuals can end up feeling lost.
This conversation will explore approaches to help learners and educators gain more insight and a feeling of place within hashtag communities. As part of this we will look at TAGS and TAGSExplorer tools (https://tags.hawksey.info) which have been developed with educators and learners in mind to help support the collection, analysis and exploration of Twitter hashtag communities. These free tools provide a means to collect data from Twitter searches and analysis the results either in Google Sheets, where the data is collected, or visualized in the companion TAGSExplorer web interface. As part of this conversation we will touch upon the limitation of data collection from Twitter and issues around data protection and privacy. We will also provide some examples of where TAGS/TAGSExplorer has been used within an educational context.
TEL Quality and Innovation: What can be learned from the history of computer ...Martin Hawksey
As TEL becomes more professionalised we consider what lessons can be learned from another discipline which has gone through a similar transition. Through the lense of the development of computer science this presentation will look at key moments in this area which might be used to inform or influence how we approach TEL quality and innovation. As part of this we will highlight the approaches adopted by early pioneers like Alan Kay whose attributed to defining the conceptual basics of laptop and tablet computers as part of his work in the 1970s on the Dynabook. Kay (2014) argues when creating future concepts the present inevitably takes all of our focus making anything we do incremental rather than inspirational. Kay’s suggests that by ignoring the present this opens us to the opportunity to take greater inspiration from the past allowing us to dream of a future not constrained by the present.
We also consider some of the cultures which have their origins in computer science including the ‘hacker’ subculture. Whilst the term ‘hacker’ has taken on a more sinister definition, referring to those subverting computer security, the original hacker communities founded by Richard Greenblatt and Bill Gosper in the 1960s were focused on the “intellectual challenge of creatively overcoming and circumventing limitations of systems to achieve novel and clever outcomes” - Wikipedia https://en.wikipedia.org/wiki/Hacker_culture
Finally, we highlight a talk by Bret Victor on the future of programming we look at the reasons a number of innovations in computer science happened in the 50s/60s and the problems this creates for the next generation of programmers if they perceive the fundamentals are correct and continue to develop along these principles.
Making the complex less complicated: An introduction to social network analysisMartin Hawksey
Presented at ILTA EdTech 2017, Sligo, Ireland
Supporting posthttps://mashe.hawksey.info/?p=17538
Patterns are left behind. Whether it be replies to a discussion forums, interactions on social media or ingredients in cocktails links can be made and the data used for actionable insight. Network science is one approach that takes these seemingly complex connections and through the use of mathematical methods make it easier to understand. Network science is a well established discipline and it’s origins can be traced to 1736 and the work of Leonhard Euler. The area of social network analysis is a more recent development established in work by Moreno and Jennings in the 1930s. Accessibility to affordable computing in the 1990s combined with data from early social networks like IRC has led to an explosion of interest in social network analysis. This has continued with the emergence of social networking sites like Facebook and Twitter combined with accessibility to the underlying data. The use of network science and social network analysis within educational contexts has seen similar growth. The emergence of ‘Learning Analytics’ as a field of study has highlighted how data can be used to enhance learning and teaching. With social network analysis we can take seemingly complex relationships and making them less complicated. Common applications of network analysis in this area include: identification of isolated students within group activities; identification of people or concepts which are ‘network bridges’; clustering of categorisation of topics; plus numerous other applications.
This presentation is designed to be an introduction into network analysis allowing delegates the opportunity to understand the underlying structure of the graph as well as some of the tools that can be used to construct them. The session will begin with an introduction to key network analysis terms and go on to introduce some of the tools and techniques for social network analysis, specifically looking at how data can be collected and analysed from Twitter using tools like TAGS and NodeXL.
Measuring Social Media Impact: Google Analytics and TwitterMartin Hawksey
Slides for a talk given at the University of Oxford OxEngage series exploring how social media interactions on Twitter can be analysed using Google Sheets and Google Analytics
Google Apps Script the Authentic{ated} Mobile PlaygroundMartin Hawksey
Presentation given at the Edinburgh Mobile Dev Meetup on 15 Feb 2017 highlighting some features of Google Apps Script which may be of interest to mobile developers. A video recording of the session is available at https://youtu.be/N9WUVzLmaJo?t=39m54s
Using CiviCRM in Google Drive with the new CiviService Google Script LibraryMartin Hawksey
This talk highlights how you can easily interact with your CiviCRM via the API interface with a new Google Apps Script library. Google Apps Script is a free programming environment in Google Drive that allows you to easily integrate with Google Docs, Forms, Sheets, other Google products and third party services. Using Apps Script gives you the flexibility and power of tools like Google Sheets to push, extract or analyse data and integrate this with your CiviCRM installation via the CiviCRM API. Example uses could include using shared Google Sheets to record information which is pushed into your CiviCRM, initiating cases from Gmail triggers and more.
he master class is designed to help users get more out of their Google Analytics setup and reporting. The session will be an opportunity to workout where you are at with you Google Analytics setup and usage. As part of this there will be an opportunity for:
* An overview of Google Analytics and tracking principles
* Learning about Google Tag Manager which can be used to remove some of the headache around setting up GA event tracking
* Automated Google Analytics reporting using Google Sheets/Google Sites
* Emerging GA uses you might not have considered before
Extracting and analyzing discussion data with google sheets and google analyticsMartin Hawksey
Online discussions can be a rich source of data for researchers in the humanities and social sciences. In this workshop, participants will learn how to use Google Sheets to push online discussion board data into Google Analytics, where it can be analysed. The session will also demonstrate how to use TAGS, the widely-used script for archiving Twitter data. Participants can bring their own laptops if they wish; there will also be desktop PCs for use.
Please note: if you’re not staff or student at the University of Edinburgh, you will need to obtain a temporary login from the registration desk in advance.
Using WordPress as a badge platform #openbadgesHEMartin Hawksey
The Association for Learning Technology has been experimenting with the open source blogging platform WordPress as an Open Badges issuing platform. As part of this presentation we include details of our journey from digital to open badges. As part of this we highlight some of the benefits of using WordPress and the free BadgeOS plugin as well as issues encountered integrating with Mozilla Backpack. As well as the technical aspect we will look at how badges were used in the Open Course for Technology Enhanced Learning (ocTEL). As part of this badges were awarded on a weekly basis for a range of tasks from simply ‘checking-in’ to completing predefined learning activities. Given the range of criteria this presentation explores the general question ‘do open badges count?’. The presentation concludes by looking at current developments which are informing how the Association might use Open Badges in the future. As part of this we will touch upon the potential other benefits of badges including situational awareness for learners and the wider community.
Tweeted slides are available from https://goo.gl/dkjI3L
Looking at creativity and culture in computer science to inspire better educa...Martin Hawksey
For talk notes see https://mashe.hawksey.info/2016/01/looking-at-creativity-and-culture-in-computer-science-to-inspire-better-education/
Academic practice continues to evolve to reflect the needs and opportunities of various stakeholders including the learner, employers and the institution. Some would argue that university education isn't changing fast enough given the pace of change within society and technology. We will explore strategies for developing an agile approach to academic practice, looking at how education can be 'hacked' to creatively overcome the limitations of the system. ... We conclude taking a wider view exploring emerging peadagogies and technologies and how these might be used too to make education better.
Google Apps Script: The authentic{ated} playground [2015 Ed.]Martin Hawksey
This is the 2015 edition of my Google Apps Script: The authentic{ated} playground talk most recently given to GDG Berlin (Dec. 2015).
With a pre-authenticated cloud-based ecosystem Google Apps Script makes it possible to integrate into other Google services with a couple of lines of code. This turns Google Drive into a rich playground for a wide range of solutions from custom reporting using Google Sheets as a data interface; quick hacks to get the job done; custom interfaces for Docs, Sheets and Forms; to full blown application deployment to web and mobile. In this talk the main features and affordances of Google Apps Script are highlighted, this will be followed by a deep dive into a demonstration into how Google Apps Script makes it easy to combine Google Analytics with other data sources such as Twitter and do many more playful things.
Learning analytics gaining good actionable insightMartin Hawksey
Presented as part of the University of Sussex's TEL Seminar Series
There is greater awareness of the use of data to make improvements in the world around us including learning and teaching. From improvements in business processes to recommendations to what to buy on Amazon all are driven by data. Data by itself does not make a better learner experience and only analytics, the process of making an actionable insight, can help identify gains. As an emerging area 'Learning Analytics' is abound with new opportunities but at the same time these opportunities also raise new ethical and operational concerns. In this presentation we introduce some basic learning analytics concepts, identifying tools and workflows staff may wish to consider. As part of this we also consider the dangers of analytics identifying areas which may lead to learner demotivation or misconception and the questions we should all be asking ourselves to make sure we are always gaining *good* actionable insight.
http://www.sussex.ac.uk/tel/workshops/seminar/martin-hawksey
Learning analytics: Threats and opportunitiesMartin Hawksey
Slides used at ALT's White Rose Learning Technologist's SIG to introduce threats and opportunities for using Learning Analytics. Links related to this presentation are at http://bit.ly/LAWhiteRose
Talk given at Using Google Apps Script and Sheets for social network data mining and analysis
Examples used in this presentation bundled at http://bit.ly/breaking-cell
There is growing interest in the use of data to provide actionable insight. This interest goes beyond the professional analysts and just as fields such as mathematics and astronomy have benefited from the enthusiastic amateur so does data science. Social networks are a rich playground of data and whilst many provide access to their data via APIs but access via this route can be daunting. You can of course turn to 'analytics as a service' sites which will take your credentials and provide you with some answers, but often this can be what they want to tell you and not what you want to hear. A solution is the spreadsheet. Spreadsheets provide an interface for data exploration for those with basic skills. With Google Sheets the opportunities increase exponentially, not just in terms of collaboration, but also with the power of Google Apps Script. Apps Script provides easy integration into other Google products and services, such as Google Analytics, as well as third party APIs like Twitter. In this presentation we show how Google Sheets can become a rich playground where data from different services can be collected and analysed.
Open Badges in Open Education – Do They Count? #eas14Martin Hawksey
Slides for presentation at e-Assessment Scotland 2014 (#eas14) highlighting the work around open badges as a mechanism for supporting the creation of personal knowledge graphs.
Slides used for presentation at ALT's Annual Conference 2014 on experiences of using open badges in the Open Course in Technology Enhanced Learning (ocTEL)
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Show me the data! Actionable insight from open courses
1. Show me the data!
Actionable insight from open courses
2. Analytics
“actionable insights through problem
definition and the application of
statistical models and analysis against
existing and/or simulated future data”
Cooper, A. 2012 – Cetis Analytics Series
What-is-Analytics-Vol1-No-5
30. Network effects
The social
network
diagrams can
be used to
identify:
• isolated
students
• group
malfunction
• users that
are
information
brokers
Hansen, D. L., Shneiderman, B., & Smith, M. (2010). Visualizing threaded conversation
networks: mining message boards and email lists for actionable insights.
32. Analytically cloaked
“Learning and knowledge creation is often
distributed across multiple media and sites in
networked environments. Traces of such
activity may be fragmented across multiple
logs and may not match analytic needs.”
Suthers, D. D., & Rosen, D. (2011).
A unified framework for multi-level analysis of distributed learning
34. In sample 41%
(n.103) emails
returned bio
% of
Total
Total Inputs
# Matched
# No Match
# Bad Input
Count
250
178 71.20%
72 28.80%
0
0.00%
35. In sample 41%
(n.103) emails
returned bio
% of
Total
API returns other
social profiles
Total Inputs
# Matched
# No Match
# Bad Input
Count
250
178 71.20%
72 28.80%
0
0.00%
36. • Detecting and Analyzing Subpopulations within
Connectivist MOOCs
• Retrospective investigation into learner
subpopulation detection within the connectivist
courses.
• Using free and open source tools we will
attempt to resolve activity data from multiple
sources to permit the analysis of any
engagement patterns.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.
What: Coursera MCQ dataWho: tutors
What: edXWho: Institutions/tutors
These trajectories are also a useful framework for thecomparison of learner engagement between different coursestructures or instructional approachesWhat: CourseraK-meansWho: Inst.
HeadacheSimply getting the data in a timely fashion
HeadacheDo you want a database table dump?Do you need to join datasets, merge results, cleanse
Ferguson and Buckingham Shum (2012)'s Social Learning Analytics: Five Approaches defines five dimensions of social learning for which one could create instruments:social network analytics — interpersonal relationships define social platformsdiscourse analytics — language is a primary tool for knowledge negotiation and constructioncontent analytics — user-generated content is one of the defining characteristics of Web 2.0disposition analytics — intrinsic motivation to learn is a defining feature of online social media, and lies at the heart of engaged learning, and innovationcontext analytics — mobile computing is transforming access to both people and content.