Slide for the workshop Learning Analytics – What Data Could Tell If We Were Willing to Share Information - with Tore Hoel, Dai Griffith, and Sally Reynolds - at EDEN conference in Barcelona 10 June 2015
Learning Analytics – Ethical questions and dilemmasTore Hoel
Workshop presentation using the Potter Box model of ethical reasoning to discuss concerns and dilemmas of Learning analytics - Open Discovery Space and Learning Analytics Community Exchange projects #laceproject #ods_eu
In May 2018, the new General Data Protection Regulation (GDPR) will enter into force in the European Union. This new regulation is considered as the most modern data protection law for Big Data societies of tomorrow. The GDPR will bring major changes to data ownership and the way data can be accessed, processed, stored, and analysed in the European Union. From May 2018 onwards, data subjects gain fundamental rights such as ‘the right to access data’ or ‘the right to be forgotten’. This will force Big Data system designers to follow a privacy-by-design approach for their infrastructures and fundamentally change the way data can be treated in the European Union.
The presentation provides an overview of the Trusted Learning Analytics Programme as it has been recently initiated at the University of Frankfurt and the DIPF research institute in Germany. Educational data is under special focus of the GDPR, as it is considered as highly sensitive like data from a nuclear plant. It shows opportunities and challenges for using educational data for learning analytics purposes under the light of the GDPR 2018.
Fighting level 3: From the LA framework to LA practice on the micro-levelHendrik Drachsler
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level). We investigated if and to what extend learning analytics dashboards are addressing educational concepts. Map opportunities and challenges for the use of Learning Analytics dashboards for the design of courses, and present an evaluation instrument for the effects of Learning Analytics called EFLA. EFLA can be used to measure the effects of LA tools at the teacher and student side. It is a robust but light (8 items) measurement to quickly investigate the level of adoption of learning analytics in a course (micro-level). The presentation concludes that Learning Analytics is still to much a computer science dicipline that does not fulfill the often claimed position of the middle space between educational and computer science research.
Learning analytics: Threats and opportunitiesMartin Hawksey
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts in order to understand and optimize the learning environment. It involves techniques from computer science, statistics, programming and other disciplines. While learning analytics can provide opportunities to give feedback and improve learning, it also poses threats regarding privacy, ethics, and the misuse of visualizations and absence of educational theory. Overall, learning analytics should be used to start conversations to improve learning rather than make definitive decisions, and it is important that the needs and experiences of learners guide its application.
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.
Xiao Hu "Overview of the Space of Learning Analytics and Educational Data Min...CITE
This document provides an overview of the fields of learning analytics and educational data mining. It discusses the types of methods used in each field, including prediction, relationship mining, and discovery with models. Recent trends are noted, such as increased emphasis on constructs like motivation and engagement, and broader data sources. Challenges and opportunities are also presented, such as improving connections between fields and addressing issues around research ethics, privacy, and data management. Upcoming conferences and a MOOC on the topic are also announced. Questions from attendees are answered, focusing on friction between fields, collaboration opportunities, and handling research ethics and privacy concerns.
Learning Analytics – Ethical questions and dilemmasTore Hoel
Workshop presentation using the Potter Box model of ethical reasoning to discuss concerns and dilemmas of Learning analytics - Open Discovery Space and Learning Analytics Community Exchange projects #laceproject #ods_eu
In May 2018, the new General Data Protection Regulation (GDPR) will enter into force in the European Union. This new regulation is considered as the most modern data protection law for Big Data societies of tomorrow. The GDPR will bring major changes to data ownership and the way data can be accessed, processed, stored, and analysed in the European Union. From May 2018 onwards, data subjects gain fundamental rights such as ‘the right to access data’ or ‘the right to be forgotten’. This will force Big Data system designers to follow a privacy-by-design approach for their infrastructures and fundamentally change the way data can be treated in the European Union.
The presentation provides an overview of the Trusted Learning Analytics Programme as it has been recently initiated at the University of Frankfurt and the DIPF research institute in Germany. Educational data is under special focus of the GDPR, as it is considered as highly sensitive like data from a nuclear plant. It shows opportunities and challenges for using educational data for learning analytics purposes under the light of the GDPR 2018.
Fighting level 3: From the LA framework to LA practice on the micro-levelHendrik Drachsler
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level). We investigated if and to what extend learning analytics dashboards are addressing educational concepts. Map opportunities and challenges for the use of Learning Analytics dashboards for the design of courses, and present an evaluation instrument for the effects of Learning Analytics called EFLA. EFLA can be used to measure the effects of LA tools at the teacher and student side. It is a robust but light (8 items) measurement to quickly investigate the level of adoption of learning analytics in a course (micro-level). The presentation concludes that Learning Analytics is still to much a computer science dicipline that does not fulfill the often claimed position of the middle space between educational and computer science research.
Learning analytics: Threats and opportunitiesMartin Hawksey
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts in order to understand and optimize the learning environment. It involves techniques from computer science, statistics, programming and other disciplines. While learning analytics can provide opportunities to give feedback and improve learning, it also poses threats regarding privacy, ethics, and the misuse of visualizations and absence of educational theory. Overall, learning analytics should be used to start conversations to improve learning rather than make definitive decisions, and it is important that the needs and experiences of learners guide its application.
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.
Xiao Hu "Overview of the Space of Learning Analytics and Educational Data Min...CITE
This document provides an overview of the fields of learning analytics and educational data mining. It discusses the types of methods used in each field, including prediction, relationship mining, and discovery with models. Recent trends are noted, such as increased emphasis on constructs like motivation and engagement, and broader data sources. Challenges and opportunities are also presented, such as improving connections between fields and addressing issues around research ethics, privacy, and data management. Upcoming conferences and a MOOC on the topic are also announced. Questions from attendees are answered, focusing on friction between fields, collaboration opportunities, and handling research ethics and privacy concerns.
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.
The document discusses using data to support student success through the DESSI project. It notes that while data is valuable, what matters most is taking action based on the data. The document outlines challenges like achieving buy-in and data quality. It explains the process of using data to inform, transform understanding, take action, and review results. Key lessons are to stay student-centered, work with available data, and have a clear vision aligned with institutional priorities of enhancing student success when using learning analytics. The document prompts questions about objectives, principles, linking to priorities, specific intended changes, and potential data sources and actions to support students.
Developing a multiple-document-processing performance assessment for epistem...Simon Knight
http://oro.open.ac.uk/41711/
The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.
Introduction to Learning Analytics for High School Teachers and ManagersVitomir Kovanovic
Presentation at the first Learning Analytics Learning Network (LALN) Event in Adelaide, Australia on Oct 22, 2019.
Abstract:
With the increased adoption of technology, institutions have unprecedented opportunities to continuously improve the quality of their services through data collection and analysis. Schools and universities now have data about learners and their contexts that can provide valuable insight into how they learn. Early attempts were directed towards mining educational data to identify students-at-risk and develop interventions. Recently, more sophisticated approaches are being deployed by researchers and practitioners. These include analysis of learner behaviour that leads to various learning outcomes, social networks and teams, employability, creativity, and critical thinking. Analysing digital traces generated through learning processes requires a broad suite of methods from data science, statistics, psychometrics, social and learning sciences.
This workshop aims to introduce teachers and educators to the fast growing and promising field of learning analytics. How digital data can be used for the analysis and improvement of student learning will be explored. First, we will provide an overview of learning analytics, its key methods and approaches, as well as problems for which it can be used. Secondly, attendees will engage in group learning activities to explore ways in which learning analytics could be used within their institutions. The focus will be on identifying learning-related challenges that are relevant to their particular context and exploring how learning analytics can be used to practically and effectively.
The document discusses putting learning first through the effective use of technology in schools. It proposes four ideas to help unlock the potential of technology and educators: 1) Plan and test innovations through building buy-in, flexibility, collaboration, and evaluation. 2) Invest long-term in educators' capacity through professional development, communication, and collaboration. 3) Make the most of education data through data literacy, privacy, and using data to generate insights rather than just accountability. 4) Pick the right partners by discussing shared values and commitment, collaborating closely, and regularly evaluating partnerships. The overall message is that technology should be implemented strategically and collaboratively to best support student learning.
A talk delivered by Stephen Pinfield at the Anybook Oxford Libraries Conference 2015 - Adapting for the Future: Developing Our Professions and Services, 21st July 2015
USING MRQAP TO ANALYSE THE DEVELOPMENT OF MATHEMATICS PRE-SERVICE TRAINEES’ C...Christian Bokhove
This document describes a study that uses social network analysis to analyze the development of communication networks among mathematics pre-service teacher trainees over time. The study collected longitudinal network data on support networks from trainees in 4 waves over 8 months. It used multiple regression quadratic assignment procedures (MRQAP) to test whether attributes like gender, training program, trust, and self-efficacy predicted the development of networks. The results found that the training program was a negative predictor of network development, and that trust was a positive predictor later in the training period. Visualizing the network changes also showed networks shifting from tight-knit to some trainees going their own way.
This document outlines a 4-step process for formulating a research problem: 1) Conceptualization which involves determining if a problem is worthwhile, timely, and solvable. 2) Gathering background information from experience, literature, and experts to identify gaps. 3) Rereading literature to find specific gaps. 4) Formulating the actual problem statement and justifying it by showing deficiencies in previous work. The example problem formulation examines how learning management systems can be used to foster writing skills at the tertiary level, justifying this by citing previous studies showing current teaching practices are insufficient and technology may provide collaborative learning opportunities.
The document discusses research assessing whether concepts from heterodox economics help students better understand real-world issues. The researchers used a mixed methods approach, including an online survey of economics students and focus group interviews, to gather data on students' experiences with and attitudes toward heterodox concepts. Preliminary survey results suggest that including heterodox economics in coursework does not necessarily lead to increased confusion and may help students understand the world better than mainstream-only approaches. The focus groups provided additional qualitative insights into which heterodox concepts students found most useful.
In England, an important role for the judgement of educational quality, is provided by the national school inspectorate Ofsted. Periodically they inspect schools and judge them. The result of the inspection is captured in inspection reports and associated documents. Ofsted has had several chief inspectors (HMCI) since 2000 and every HMCI tends to put his/her own mark on the inspectorate. This paper extends the analysis of the corpus in Author (2020) using the corpus of more than 17,000 Ofsted documents which were scraped from their website with text-mining techniques. Using the computational research method of structural topic modelling I re-analyse a set of documents that typically could not be analysed with manual methods. I juxtapose the findings with previous findings from sentiment analyses. The paper does not just cover the substantive topic at hand, but also provide insight in how the methods work, and how they provide insight in policy shifts during the ‘reign’ of different HMCIs. All in all, we can see how such text-mining techniques allow us to analyse existing documents at scale.
An Intrusion Detection based on Data mining technique and its intended import...Editor IJMTER
Intrusion detection is a pivotal and essential requirement of today’s era. There are two
major side of Intrusion detection namely, Host based intrusion detection as well as network based
intrusion detection. In Host based intrusion detection system, it monitors the information arrive at the
particular machine or node. While in network based intrusion system, it monitor and analyze whole
traffic of network. Data mining introduce latest technology and methods to handle and categorize
types of attacks using different classification algorithm and matching the patterns of malicious
behavior. Due to the use of this data mining technology, developers extract and analyze the types of
attack in the network.
In addition to this there are two major approach of intrusion detection. First, anomaly based approach,
in which attacks are found with high false alarm rate. However, in signature based approach, false
alarm rate is low with lack of processing of novel attacks. Most of the researchers do their research
based on signature intrusion with the purpose to increase detection rate. Major advantage of this
system, IDS does not require biased assessment and able to identify massive pattern of attacks.
Moreover, capacity to handle large connection records of network. In this paper we try to discover
the features of intrusion detection based on data mining technique.
Learning Analytics – Opportunities for ISO/IEC JTC 1/SC36 standardisationTore Hoel
1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models.
2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers.
3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.
This document describes a PhD thesis that focuses on developing host-based and network-based anomaly detectors for HTTP attacks. Specifically, it presents three contributions: (1) McPAD, a multiple classifier system for network-based payload anomaly detection; (2) HMMPayl, which uses hidden Markov models for payload analysis; and (3) HMM-Web, which analyzes request URIs for host-based anomaly detection. The thesis evaluates the performance of these approaches on detection rate, false positive rate, and area under the ROC curve.
Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules, which only allow one out of multiple activities to be performed. These methods assume that decision making is fully deterministic, and all factors influencing decisions are recorded. In case the underlying decision rules are overlapping due to non-determinism or incomplete information, the rules returned by existing methods do not fit the recorded data well. This paper proposes a new technique to discover overlapping decision rules, which fit the recorded data better at the expense of precision, using decision tree learning techniques. An evaluation of the method on two real-life data sets confirms this trade off. Moreover, it shows that the method returns rules with better fitness and precision in under certain conditions.
Original paper: http://dx.doi.org/10.1007/978-3-319-39696-5_23
Presented at CAiSE'16
MATLAB Implementation of Scan-to-Scan Discriminator for the Detection of Mari...IJERD Editor
For an operational radar, backscatter of the transmitted signal by the elements of the sea surface
often places severe limits on the detect ability of returns from ships, missiles, navigation buoys, and other
targets sharing the radar resolution cell with the sea surface. These interfering signals are commonly referred to
as sea clutter, or sea echo. Maritime surveillance radar experiences serious limitations imposed on their
performance by unwanted sea clutter.
In the marine environment, sea clutter might occur at a random position in one scan but not in the next or
subsequent scans. In contrast, targets will appear from scan to scan with essentially the same amplitude. The
scan-to-scan discriminator eliminates “spiky” clutter by using one of the 3 approaches. They are:
1. Fixed Threshold.
2. Cell Averaging Constant False Alarm Rate (CA- CFAR).
3. Clutter Map.
The output of the scan-to-scan discriminator is then used to tag the speed of small marine targets and gating
approach is used to tag the speed of large marine targets.
The proposed architectures have been designed using MATLAB R2007b.
From Low-Level Events to Activities - A Pattern based ApproachFelix Mannhardt
Presentation at BPM'16 on our paper 'From Low-Level Events to Activities - A Pattern based Approach'. Published at dx.doi.org/10.1007/978-3-319-45348-4_8
The document discusses key challenges in the field of learning analytics, including connecting analytics to pedagogy and learning science, developing ethical guidelines, focusing on learner perspectives, and addressing issues of consent, privacy, equality and data ownership. It presents ten reflection questions to prompt thinking on these challenges, such as how pedagogy links to analytics work, the problems analytics aim to solve for learners, important ethical decisions made, and potential changes in response to the challenges. Six core challenges are also summarized: building learning science connections, using diverse data sets, considering learner views, establishing ethics protocols, ensuring consent and safeguarding, and promoting equality and data control.
Keynote talk given at the Learning Analytics Summer Institute 2016 (LASI16) at the University of Deusto, Bilbao, Spain in June 2016 by Rebecca Ferguson.
What does the future hold for learning analytics? In terms of Europe’s priorities for learning and training, they will need to support relevant and high-quality knowledge, skills and competences developed throughout lifelong learning. More specifically, they should improve the quality and efficiency of education and training, enhance creativity and innovation, and focus on learning outcomes in areas such as employability, active-citizenship and well-being. This is a tall order and, in order to achieve it, we need to consider how our work fits into the larger picture. Drawing on the outcomes of two recent European studies, Rebecca will discuss how we can avoid potential pitfalls and develop an action plan that will drive the development of analytics that enhance both learning and teaching.
Conclusions – the future of ideologiesJudging ideol.docxmccormicknadine86
Conclusions – the future of ideologies?
Judging ideologies
What is the “best” ideology? Why?
Categories
Change: Reform/Revolution
Authority: Place of Individual/State
Free Will/Volunteerism versus Determinism
Human Nature
Good and/or Evil Competition and/or Cooperation
Equality and Greatest Freedom
Basis of Society (political, gender, religion, economic)
The “beast”/reaction against
Future of ideologies?
Ideologies – social transformation and political development = ongoing cycle?
“End of Ideology” – Endism?
What drives the formation of ideologies?
Democracy – the best?
“Democracy is the worst form of government except all those other forms that have been tried from time to time” Winston Churchill
Future of ideologies? (continued)
Newly Emerging Ideologies
Globalism (globalization = process)
Connectivity/Interconnectedness
Global trends
Identity
Signs
Critiques
Future of ideologies? (continued)
Newly (re) Emerging Ideologies
Localism
Limited Connectivity / Local Identity
Populism
Heywood, p. 291 “”the belief that the instincts and wishes of the people provide the principle legitimate guide to political action”
Authority with “the people” / assumptions about elite
Future of ideologies? (continued)
Trumpism?
Trends
Immigration
Working Class
Divides – rural/urban, conservative/liberal
Increasing polarization
CA3
Naomi Kendal
77
Decide
● What industry/business are you in?
● eg HR, Marketing, Sales, Retail, Software development
● As we move through each topic, you must apply it to your business.
● Research – eg how does my company collect/ store/ analyse Big
Data…
● How does my company use AI, what are the benefits…
● Find case studies from your specific industry and research to back
it up.
● Visualisations to illustrate the meanings – of 3 topics.
● Trends & Recommendations
CA3 Assessment Brief
Module Title: Data & Digital Marketing Analytics
Module Code: B9DM105
Module Leader: MSc in Digital Marketing
Stage (if relevant): 9
Assessment Title: Data: Full report and analysis.
Assessment Number (if relevant): CA 3 of 3
Assessment Type: Project Report
Individual/Group: Individual
Assessment Weighting: 40%
Issue Date: 21/6/19
Hand In Date: 07/8/19
Mode of Submission: Moodle
Details of Assignment brief
You are working in the Big Data Dept. of the ‘Red Cloud’ company – a large
multinational, reporting to the newly appointed CEO.
As she is new to the role, she would like a report (3000 words) explaining the
Information Management strategy of the company.
You should include the following topics in this report:
Data Analytics, Data Collection & Storage and the methods and technologies used in
analysing the Data.
Data Abstraction Layers, Data Warehousing and Data Mining.
The role and benefits of your Big Data Dept prior to a restructure.
She also wants a Data Visualisation to illustrate a relevant Data Set for 3 of the
below headings.
1. Data analytics – ...
1) The document discusses three paradigmatic positions that institutions may take regarding ethics and privacy in learning analytics: proceed with caution and respect existing policies, proceed with caution while still trying to be respectful, or adopt a data-driven approach and adapt policies accordingly.
2) Technical infrastructure is a major concern, as it can constrain or determine an institution's data policies. Systems developed by commercial platforms may not prioritize privacy and individual control.
3) The discussion activity prompts reflection on an institution's current position, any conflicts between stakeholder views, the technical systems that influence policy, and open questions about technology and privacy.
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.
The document discusses using data to support student success through the DESSI project. It notes that while data is valuable, what matters most is taking action based on the data. The document outlines challenges like achieving buy-in and data quality. It explains the process of using data to inform, transform understanding, take action, and review results. Key lessons are to stay student-centered, work with available data, and have a clear vision aligned with institutional priorities of enhancing student success when using learning analytics. The document prompts questions about objectives, principles, linking to priorities, specific intended changes, and potential data sources and actions to support students.
Developing a multiple-document-processing performance assessment for epistem...Simon Knight
http://oro.open.ac.uk/41711/
The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory.
Introduction to Learning Analytics for High School Teachers and ManagersVitomir Kovanovic
Presentation at the first Learning Analytics Learning Network (LALN) Event in Adelaide, Australia on Oct 22, 2019.
Abstract:
With the increased adoption of technology, institutions have unprecedented opportunities to continuously improve the quality of their services through data collection and analysis. Schools and universities now have data about learners and their contexts that can provide valuable insight into how they learn. Early attempts were directed towards mining educational data to identify students-at-risk and develop interventions. Recently, more sophisticated approaches are being deployed by researchers and practitioners. These include analysis of learner behaviour that leads to various learning outcomes, social networks and teams, employability, creativity, and critical thinking. Analysing digital traces generated through learning processes requires a broad suite of methods from data science, statistics, psychometrics, social and learning sciences.
This workshop aims to introduce teachers and educators to the fast growing and promising field of learning analytics. How digital data can be used for the analysis and improvement of student learning will be explored. First, we will provide an overview of learning analytics, its key methods and approaches, as well as problems for which it can be used. Secondly, attendees will engage in group learning activities to explore ways in which learning analytics could be used within their institutions. The focus will be on identifying learning-related challenges that are relevant to their particular context and exploring how learning analytics can be used to practically and effectively.
The document discusses putting learning first through the effective use of technology in schools. It proposes four ideas to help unlock the potential of technology and educators: 1) Plan and test innovations through building buy-in, flexibility, collaboration, and evaluation. 2) Invest long-term in educators' capacity through professional development, communication, and collaboration. 3) Make the most of education data through data literacy, privacy, and using data to generate insights rather than just accountability. 4) Pick the right partners by discussing shared values and commitment, collaborating closely, and regularly evaluating partnerships. The overall message is that technology should be implemented strategically and collaboratively to best support student learning.
A talk delivered by Stephen Pinfield at the Anybook Oxford Libraries Conference 2015 - Adapting for the Future: Developing Our Professions and Services, 21st July 2015
USING MRQAP TO ANALYSE THE DEVELOPMENT OF MATHEMATICS PRE-SERVICE TRAINEES’ C...Christian Bokhove
This document describes a study that uses social network analysis to analyze the development of communication networks among mathematics pre-service teacher trainees over time. The study collected longitudinal network data on support networks from trainees in 4 waves over 8 months. It used multiple regression quadratic assignment procedures (MRQAP) to test whether attributes like gender, training program, trust, and self-efficacy predicted the development of networks. The results found that the training program was a negative predictor of network development, and that trust was a positive predictor later in the training period. Visualizing the network changes also showed networks shifting from tight-knit to some trainees going their own way.
This document outlines a 4-step process for formulating a research problem: 1) Conceptualization which involves determining if a problem is worthwhile, timely, and solvable. 2) Gathering background information from experience, literature, and experts to identify gaps. 3) Rereading literature to find specific gaps. 4) Formulating the actual problem statement and justifying it by showing deficiencies in previous work. The example problem formulation examines how learning management systems can be used to foster writing skills at the tertiary level, justifying this by citing previous studies showing current teaching practices are insufficient and technology may provide collaborative learning opportunities.
The document discusses research assessing whether concepts from heterodox economics help students better understand real-world issues. The researchers used a mixed methods approach, including an online survey of economics students and focus group interviews, to gather data on students' experiences with and attitudes toward heterodox concepts. Preliminary survey results suggest that including heterodox economics in coursework does not necessarily lead to increased confusion and may help students understand the world better than mainstream-only approaches. The focus groups provided additional qualitative insights into which heterodox concepts students found most useful.
In England, an important role for the judgement of educational quality, is provided by the national school inspectorate Ofsted. Periodically they inspect schools and judge them. The result of the inspection is captured in inspection reports and associated documents. Ofsted has had several chief inspectors (HMCI) since 2000 and every HMCI tends to put his/her own mark on the inspectorate. This paper extends the analysis of the corpus in Author (2020) using the corpus of more than 17,000 Ofsted documents which were scraped from their website with text-mining techniques. Using the computational research method of structural topic modelling I re-analyse a set of documents that typically could not be analysed with manual methods. I juxtapose the findings with previous findings from sentiment analyses. The paper does not just cover the substantive topic at hand, but also provide insight in how the methods work, and how they provide insight in policy shifts during the ‘reign’ of different HMCIs. All in all, we can see how such text-mining techniques allow us to analyse existing documents at scale.
An Intrusion Detection based on Data mining technique and its intended import...Editor IJMTER
Intrusion detection is a pivotal and essential requirement of today’s era. There are two
major side of Intrusion detection namely, Host based intrusion detection as well as network based
intrusion detection. In Host based intrusion detection system, it monitors the information arrive at the
particular machine or node. While in network based intrusion system, it monitor and analyze whole
traffic of network. Data mining introduce latest technology and methods to handle and categorize
types of attacks using different classification algorithm and matching the patterns of malicious
behavior. Due to the use of this data mining technology, developers extract and analyze the types of
attack in the network.
In addition to this there are two major approach of intrusion detection. First, anomaly based approach,
in which attacks are found with high false alarm rate. However, in signature based approach, false
alarm rate is low with lack of processing of novel attacks. Most of the researchers do their research
based on signature intrusion with the purpose to increase detection rate. Major advantage of this
system, IDS does not require biased assessment and able to identify massive pattern of attacks.
Moreover, capacity to handle large connection records of network. In this paper we try to discover
the features of intrusion detection based on data mining technique.
Learning Analytics – Opportunities for ISO/IEC JTC 1/SC36 standardisationTore Hoel
1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models.
2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers.
3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.
This document describes a PhD thesis that focuses on developing host-based and network-based anomaly detectors for HTTP attacks. Specifically, it presents three contributions: (1) McPAD, a multiple classifier system for network-based payload anomaly detection; (2) HMMPayl, which uses hidden Markov models for payload analysis; and (3) HMM-Web, which analyzes request URIs for host-based anomaly detection. The thesis evaluates the performance of these approaches on detection rate, false positive rate, and area under the ROC curve.
Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules, which only allow one out of multiple activities to be performed. These methods assume that decision making is fully deterministic, and all factors influencing decisions are recorded. In case the underlying decision rules are overlapping due to non-determinism or incomplete information, the rules returned by existing methods do not fit the recorded data well. This paper proposes a new technique to discover overlapping decision rules, which fit the recorded data better at the expense of precision, using decision tree learning techniques. An evaluation of the method on two real-life data sets confirms this trade off. Moreover, it shows that the method returns rules with better fitness and precision in under certain conditions.
Original paper: http://dx.doi.org/10.1007/978-3-319-39696-5_23
Presented at CAiSE'16
MATLAB Implementation of Scan-to-Scan Discriminator for the Detection of Mari...IJERD Editor
For an operational radar, backscatter of the transmitted signal by the elements of the sea surface
often places severe limits on the detect ability of returns from ships, missiles, navigation buoys, and other
targets sharing the radar resolution cell with the sea surface. These interfering signals are commonly referred to
as sea clutter, or sea echo. Maritime surveillance radar experiences serious limitations imposed on their
performance by unwanted sea clutter.
In the marine environment, sea clutter might occur at a random position in one scan but not in the next or
subsequent scans. In contrast, targets will appear from scan to scan with essentially the same amplitude. The
scan-to-scan discriminator eliminates “spiky” clutter by using one of the 3 approaches. They are:
1. Fixed Threshold.
2. Cell Averaging Constant False Alarm Rate (CA- CFAR).
3. Clutter Map.
The output of the scan-to-scan discriminator is then used to tag the speed of small marine targets and gating
approach is used to tag the speed of large marine targets.
The proposed architectures have been designed using MATLAB R2007b.
From Low-Level Events to Activities - A Pattern based ApproachFelix Mannhardt
Presentation at BPM'16 on our paper 'From Low-Level Events to Activities - A Pattern based Approach'. Published at dx.doi.org/10.1007/978-3-319-45348-4_8
The document discusses key challenges in the field of learning analytics, including connecting analytics to pedagogy and learning science, developing ethical guidelines, focusing on learner perspectives, and addressing issues of consent, privacy, equality and data ownership. It presents ten reflection questions to prompt thinking on these challenges, such as how pedagogy links to analytics work, the problems analytics aim to solve for learners, important ethical decisions made, and potential changes in response to the challenges. Six core challenges are also summarized: building learning science connections, using diverse data sets, considering learner views, establishing ethics protocols, ensuring consent and safeguarding, and promoting equality and data control.
Keynote talk given at the Learning Analytics Summer Institute 2016 (LASI16) at the University of Deusto, Bilbao, Spain in June 2016 by Rebecca Ferguson.
What does the future hold for learning analytics? In terms of Europe’s priorities for learning and training, they will need to support relevant and high-quality knowledge, skills and competences developed throughout lifelong learning. More specifically, they should improve the quality and efficiency of education and training, enhance creativity and innovation, and focus on learning outcomes in areas such as employability, active-citizenship and well-being. This is a tall order and, in order to achieve it, we need to consider how our work fits into the larger picture. Drawing on the outcomes of two recent European studies, Rebecca will discuss how we can avoid potential pitfalls and develop an action plan that will drive the development of analytics that enhance both learning and teaching.
Conclusions – the future of ideologiesJudging ideol.docxmccormicknadine86
Conclusions – the future of ideologies?
Judging ideologies
What is the “best” ideology? Why?
Categories
Change: Reform/Revolution
Authority: Place of Individual/State
Free Will/Volunteerism versus Determinism
Human Nature
Good and/or Evil Competition and/or Cooperation
Equality and Greatest Freedom
Basis of Society (political, gender, religion, economic)
The “beast”/reaction against
Future of ideologies?
Ideologies – social transformation and political development = ongoing cycle?
“End of Ideology” – Endism?
What drives the formation of ideologies?
Democracy – the best?
“Democracy is the worst form of government except all those other forms that have been tried from time to time” Winston Churchill
Future of ideologies? (continued)
Newly Emerging Ideologies
Globalism (globalization = process)
Connectivity/Interconnectedness
Global trends
Identity
Signs
Critiques
Future of ideologies? (continued)
Newly (re) Emerging Ideologies
Localism
Limited Connectivity / Local Identity
Populism
Heywood, p. 291 “”the belief that the instincts and wishes of the people provide the principle legitimate guide to political action”
Authority with “the people” / assumptions about elite
Future of ideologies? (continued)
Trumpism?
Trends
Immigration
Working Class
Divides – rural/urban, conservative/liberal
Increasing polarization
CA3
Naomi Kendal
77
Decide
● What industry/business are you in?
● eg HR, Marketing, Sales, Retail, Software development
● As we move through each topic, you must apply it to your business.
● Research – eg how does my company collect/ store/ analyse Big
Data…
● How does my company use AI, what are the benefits…
● Find case studies from your specific industry and research to back
it up.
● Visualisations to illustrate the meanings – of 3 topics.
● Trends & Recommendations
CA3 Assessment Brief
Module Title: Data & Digital Marketing Analytics
Module Code: B9DM105
Module Leader: MSc in Digital Marketing
Stage (if relevant): 9
Assessment Title: Data: Full report and analysis.
Assessment Number (if relevant): CA 3 of 3
Assessment Type: Project Report
Individual/Group: Individual
Assessment Weighting: 40%
Issue Date: 21/6/19
Hand In Date: 07/8/19
Mode of Submission: Moodle
Details of Assignment brief
You are working in the Big Data Dept. of the ‘Red Cloud’ company – a large
multinational, reporting to the newly appointed CEO.
As she is new to the role, she would like a report (3000 words) explaining the
Information Management strategy of the company.
You should include the following topics in this report:
Data Analytics, Data Collection & Storage and the methods and technologies used in
analysing the Data.
Data Abstraction Layers, Data Warehousing and Data Mining.
The role and benefits of your Big Data Dept prior to a restructure.
She also wants a Data Visualisation to illustrate a relevant Data Set for 3 of the
below headings.
1. Data analytics – ...
1) The document discusses three paradigmatic positions that institutions may take regarding ethics and privacy in learning analytics: proceed with caution and respect existing policies, proceed with caution while still trying to be respectful, or adopt a data-driven approach and adapt policies accordingly.
2) Technical infrastructure is a major concern, as it can constrain or determine an institution's data policies. Systems developed by commercial platforms may not prioritize privacy and individual control.
3) The discussion activity prompts reflection on an institution's current position, any conflicts between stakeholder views, the technical systems that influence policy, and open questions about technology and privacy.
This document discusses educational leadership and ICT policy. It defines policy and discusses key elements of policy, including problem definition, goals, and instruments. It also discusses analyzing policy statements through different frameworks such as logical analysis of internal consistency. The document provides examples of analyzing ICT in education policies and discusses using a self-review framework to evaluate how schools integrate technology.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
2018 EAPRIL new organizational learning strategy and cloudTom De Schryver
Presentation used at the 2018 conference ,Piran, Slovenia.
Presentation updated (two slides added on boundary objects) after private conversations with one of our keynote speakers.
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.
This document summarizes a presentation on learning analytics given at the ALT-C 2014 conference in Warwick, UK. It discusses the Learning Analytics Community Exchange (LACE) project, which is a 24 month EU support project with 9 partners focusing on implementing learning analytics in schools, higher education, and industry. The presentation defines learning analytics as the measurement, collection, analysis and reporting of learner data to understand and optimize learning. It also discusses cultural and technical challenges around topics such as data privacy, change management, and making insights actionable. Examples of learning analytics tools described include SNAPP for social networks and LOCO-Analyst.
This document discusses potential future innovations in pedagogy and education. It begins by providing context on rapid technological changes and the need to prepare students for future careers. It then outlines several pedagogical innovations that have emerged in recent years such as learning analytics, spaced learning, computational thinking, and flipped classrooms. These innovations leverage new technologies and draw on research from fields like neuroscience and computer science. The document concludes by discussing approaches for identifying new promising pedagogies, such as expert workshops, provocations, and scenario development using a Policy Delphi method.
The document discusses visions for the future of learning analytics based on a presentation given by Rebecca Ferguson. It outlines several potential futures for learning analytics, including learners being monitored by their learning environments, learners' personal data being tracked, and learners controlling their own data. It also discusses various challenges regarding ethics, regulation, validity, and affect that will need to be addressed for learning analytics to achieve its potential while avoiding negative consequences. The overall message is that learning analytics show promise to improve education if developed and applied carefully and ethically with student well-being and consent as top priorities.
This document provides an overview of pedagogical innovations presented at the JTEL Summer School in Bari, Italy on June 4, 2019. It discusses 10 potential big ideas for transforming education, including spaced learning, computational thinking, epistemic education, threshold concepts, flipped classroom, intergroup empathy, making thinking visible, learning with robots, decolonizing learning, and event-based learning. Each idea is summarized briefly, outlining its key aspects and providing examples of how it could be implemented using technology to enhance learning.
Open Education 2011: Openness and Learning AnalyticsJohn Rinderle
1) The document discusses using learning analytics to improve open educational resources (OER) by creating feedback loops between data collection, analysis, and continuous improvement of OER.
2) It proposes developing common data standards, analytics-enabled OER, and shared analytics platforms to better measure the effectiveness and impact of OER.
3) The goal is to turn teaching into a community-based research activity where OER are continuously evaluated and improved based on data and evidence of student learning outcomes.
Introduction to Learning Analytics. Slides for Tutorial 1 led by Rebecca Ferguson at the Learning Analytics Summer Institute (LASI), June 2022, hosted online by the Society for Learning Analytics Research (SoLAR) with the University of British Columbia.
Read 290 Critical Reading as Critical ThinkingOnlineWeek .docxcatheryncouper
Read 290: Critical Reading as Critical Thinking
Online
Week 6
This week you will be taking the quiz covering chapters 1 through 5 from the ARQ text. You will also be taking Exam #1 on Friday. The following checklist should help you to stay organized and focused on your online assignments.
Week 6 – Checklist
1. View the Week 6 video announcement or Read the Week 6 announcement to get an overview of this week’s assignments.
2. Take the quiz on Chapters 1 through 5 in the ARQ text. The quiz consists of 25 multiple choice and true/false questions. It should not take more than 1 hour to complete. The quiz is due by Wednesday at 11:59pm.
3. Prepare for Exam #1 by completing the following steps:
a) Read the article “It’s a Job for Parents, Not the Government”
b) View the Exam 1 – Sample Analysis Presentation
4. Take Exam #1. The exam will be available from 12:00am until 11:55pm on Friday. You will have 2 hours to complete the exam. You will need to complete the following steps:
a) Read the Exam 1 Article
b) Analyze the article by completing the Exam 1 Worksheet
c) Access and complete the Exam. Use your worksheet to answer the questions about the article. Then submit your exam.
d) Submit your worksheet using the link provided in the week 6 section of TITANium. You must submit your worksheet to get credit for this exam.
Good luck!
Week Seven: Managing Knowledge
Information Resource Management
IT620
February 22, 2014
Running head: MANAGING KNOWLEDGE 1
MANAGING KNOWLEDGE 3
MANAGING KNOWLEDGE 2
Managing Knowledge
Chapter 14: Question 3. How do human capital, structural capital, and customer capital differ?
ANSWER.
Human capital, structural capital and customer capital are three basic fundamentals of any organization. They differ in their roles playing for that organization. Human capital is the efficiency of a human being on the basis of its skills; more the human is skilled and efficient results in more human capital. On the structural capital is the efficiency of the organizations to provide environment in which they can increase the efficiencies of humans working for them such as data, systems, knowledge and designs. The customer capital is the relationship bond with organization products, stronger the bond between the customer and the products results in a stronger customer capital.
Chapter 14: Question 8. What approach did the energy company take to encourage knowledge sharing among its 15 business units?
ANSWER.
The approach that the energy company took to encourage sharing among its 15 business units was establishing peer groups from various units. The idea was to have employees share his or her knowledge without the involvement of leadership to avoid political aspects. Since this was unsuccessful it was decided to provide a human portal for employees where they can ask questions regarding the problems they faced with their customers and employees from other business units provides a solution to them, its kind ...
Innnovations in online teaching and learning: CHatGPT and other artificial as...Rebecca Ferguson
Talk given by Agnes Kukulska-Hulme and Rebecca Ferguson to SciLab (a centre for pedagogical research and innovation in business and law) at The Open University, Milton Keynes, UK on Wednesday 3 May 2023.
The document discusses Apereo, a non-profit foundation that supports open source education technologies. It provides an overview of Apereo's mission, activities, software communities, and communities of interest. It also discusses priorities such as an open learning analytics platform and the need for an interoperable next generation digital learning environment that goes beyond course-centered learning management systems. Open source is seen as critical to realizing this vision through interoperability, reference models, and sustaining diverse communities that can innovate.
This document provides an overview of qualitative market research methods. It discusses focus groups, depth interviews, online qualitative research, sampling approaches, analysis techniques, and ethics. The key goals of qualitative research are to understand why people say and do certain things, interpret their meanings, and develop useful explanations rather than objective truths. Common qualitative methods include focus groups, depth interviews, observations, and online discussions.
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.
Similar to Workshop on Learning Analytics @ EDEN15 in Barcelona - June 2015 (20)
This document discusses challenges for the higher education sector in implementing learning analytics at scale. It begins with an overview of learning analytics and its potential uses. Key challenges mentioned include developing a consensus approach for Norway, addressing privacy issues, establishing infrastructure for data handling and analysis, and developing standards and competencies. The document calls for establishing several resources to help institutions, including tools for assessing readiness, developing strategies, conducting research, and managing data and analytics processes according to privacy standards.
Smart Learning Environments - a framework for standardisation?Tore Hoel
1) Smart learning environments (SLEs) could provide a framework for standardizing learning technologies, but the term "smart" is problematic without a clear definition.
2) Existing SLE frameworks from researchers like Koper could structure standards work but may not reflect market needs.
3) Taking a pragmatic approach by developing smaller, self-contained standards informed by—but not dictated by—SLE frameworks may be more effective than large, multipart standards.
Learning analytics in a standardisation contextTore Hoel
This document discusses learning analytics in the context of standardization. It begins by outlining 5 forces that are changing the world: access to data, viewing student data as a resource to be mined and used, concerns about student privacy, and using technology like fitbits and social media monitoring to track students. It then discusses key concepts in learning analytics like definitions, benefits for learners, teachers, and institutions. Other topics covered include the learning analytics process, addressing privacy and data protection, and the potential role of standardization in learning analytics.
Data protection and privacy framework in the design of learning analytics sys...Tore Hoel
Presentation on The Influence of Data Protection and Privacy Frameworks on the Design of Learning Analytics Systems at LAK17, Vancouver, Canada - 2017-03-16
Data Protection by Design and Default for Learning AnalyticsTore Hoel
The Principle of Data Protection by Design and Default as a lever for bringing Pedagogy into the Discourse on Learning Analytics. Workshop presentation at ICCE 2016 conference in Mumbai, India 29 November 2016
Scaling up learning analytics solutions: Is privacy a show-stopper?Tore Hoel
1) The document discusses the challenges of scaling up learning analytics solutions from research labs to the classroom in light of privacy and ethics concerns.
2) It notes that learning analytics could be considered unlawful if students do not have control over and consent to how their data is used.
3) The presentation raises important questions about data ownership, student consent, and limiting data collection and use to only what is necessary for educational purposes.
Implications of the European Data Protection Regulations for Learning Analyti...Tore Hoel
The document discusses the implications of the European Union's General Data Protection Regulation (GDPR) for the design of learning analytics (LA) tools. It analyzes how each stage of the LA process, from data collection to feedback, is affected by the GDPR's new regulations around user consent, data access and portability, security, and privacy by design. The authors argue the GDPR gives designers a two-year window to build LA tools that comply through open architectures, personal data stores, and conversational capabilities respecting user rights. They call for further comparative studies of different regions' approaches to help guide responsible and privacy-aware LA development.
Privacy and Data Protection - principles for design of a new part of an ISO s...Tore Hoel
This document discusses privacy and data protection concerns regarding learning analytics and efforts to address them. It outlines several privacy attributes that should be considered when designing learning analytics systems, including the right to be informed, access, rectification, erasure, restricted processing, data portability, and objection. It also discusses requirements around automated decision making, profiling, accountability, and data breaches. The goal is to provide guidance for developing learning analytics tools and architectures that uphold learner privacy within a two year window of opportunity.
Towards Open Architectures and Interoperability for Learning Analytics Tore Hoel
Tore Hoel presented on the need for open architectures and interoperability standards for learning analytics. Key challenges include a lack of trust, different data schemas and sources being used, and privacy and data ownership issues. Standards are needed for activity streams, vocabularies, storage designs, and algorithms. While initiatives exist, there is no single European leader coordinating standardization efforts for learning analytics.
Learning Analytics - Vision of the FutureTore Hoel
The document summarizes a presentation about the future of learning analytics given by Tore Hoel. Some key points:
- The presentation discusses the European LACE project, which aims to integrate communities working on learning analytics in schools, workplaces and universities.
- Seven visions of the future of learning analytics in 2025 are presented, including scenarios where analytics are used for educational management, support self-directed learning, or are rarely used due to privacy and data issues.
- The presentation concludes by discussing open systems for learning analytics and international communities focused on this topic.
Data security issues, ethical issues and challenges to privacy in knowledge-i...Tore Hoel
Presentation at “Finnish-Norwegian Workshop in Learning Analytics”, Helsinki, 21-22 May 2015. Organised by The Research Council of Norway and Academy of Finland
The document discusses requirements for learning analytics based on a lecture and workshop at East China Normal University. It begins with introductions and then outlines the day's plan to discuss definitions of analytics, actors in learning analytics, framework models, and requirements. It emphasizes starting with pedagogy and poses questions about what data is available and how to build trust. Ethical challenges are noted around data protection, privacy, transparency, and purpose. The goal is to use analytics to facilitate learning while avoiding instructivist approaches and stress for learners.
Introduction to Learning Analytics - Framework and Implementation ConcernsTore Hoel
This document provides an introduction to learning analytics, including:
1. A definition of learning analytics as the measurement, collection, analysis and reporting of learner data to understand and optimize learning.
2. An overview of how learning analytics is used in universities, schools, and the workplace to predict student performance, track progress, and personalize instruction.
3. A framework model showing how data is transformed into analytics and insights to benefit learners, teachers, and institutions.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Digital Artefact 1 - Tiny Home Environmental Design
Workshop on Learning Analytics @ EDEN15 in Barcelona - June 2015
1. Learning Analytics – What Data Could Tell
If We Were Willing to Share Information
Dai Griffiths, University of Bolton, UK
Tore Hoel,Oslo and Akershus University College, Norway
Sally Reynolds, ATiT, Belgium
2. Workshop Structure
• Aim: to answer the question:What Data Could
Tell If We Were Willing to Share Information
• Introductions
• Overview of Learning Analytics landscape
• Break-out group 1: what are the challenges
and opportunities
• Report back
• Break-out group 2: what data are we willing to
share and under what challenges
• Report back
2
3. Learning Analytics – What Data Could Tell
If We Were Willing to Share Information
Scoping the discussion
Dai Griffiths, University of Bolton
d.e.griffiths@bolton.ac.uk
4. Learning Analytics…
It’s all true, but what is missing here?
Learning analytics (LA) is a multi-disciplinary field involving
• machine learning
• artificial intelligence
• information retrieval
• statistics
• visualization
LA is also a field in which several related areas of research in TEL converge. These include
• academic analytics
• action research
• educational
• data mining
• recommender systems
• personalized adaptive learning.
Chatti et al. (2012) A Reference Model for Learning Analytics, IJTEL, Vol. 4, Nos. 5/ pp.318–331.
4
5. This is a Normal Slide
• Your main slides would go here.
• Note that the slides shown previously and after this slide may be
used across a number of slide shows during the event for
example the Welcome slides may include those shown before
this one and the Conclusions session may include the following
slides.
5
6. Learning Analytics – What Data Could Tell...
three approaches
1) Institutional management and reporting
–Builds on business intelligence and customer relationship managment.
–Educational Management Analytics? Easy to do, but usually depends on
KPI’s being a good reflection of learning and effective education.
2) Reflective learner/teacher
–Builds on ‘reflective practitioner’, communities of practice, social network
analysis,
–Study/teaching analytics? The most interesting, but policy and politics can
be problematic.
3) Improve our understanding of learning, to enhance or automate
teaching
–Builds on the methods mentioned in the earlier slide.
–Yes, Learning Analytics. Exciting research, but hard to achieve.
We should be clear which we are talking about. Does the word ‘learning’ fog the
issue?
6
7. Learning Analytics – What Data Could Tell If
We Were Willing to Share Information
Who does the data belong to?
Sandy Pentland has been promoting a ‘New Deal on Data’, under
which “People would have the same rights they now have over their
physical bodies and money”.
Harvard Business Review, November 2014.
• Is he plain stupid? (clue: he is the Toshiba Professor at MIT and
on the boards of Telefonica, Motorola Mobile, and Nissan)
• Is it more reasonable to let anyone grab what they can?
• What would this mean for education?
• Societies, institutions and individuals need to take a position on
this, and think what it means for Learning Analytics
7
8. What would make us willing?
• What are we being offered? We should remember that
– Learning Analytics is often concerned with matters other than
learning.
– LA often uses methods which derive from management rather
than from teaching.
•These are not bad characteristics, but
– what kind of analytics we are talking about at any time?
– Increasing managerialism is part of a wider socio-economic
and political shift. It is contested. Our ‘willingness’ will be tied
up with these themes.
•What is the deal?
– Who are the beneficiaries?
– Who is trading what, and what is offered in exchange? 8
9. “Learning Analytics – What Data Could Tell If We Were Willing to
Share Information: scoping the discussion” by Dai Griffiths was
presented at EDEN 2015.
d.e.griffiths@bolton.ac.uk
This work was undertaken as part of the LACE Project, supported by the European Commission Seventh
Framework Programme, grant 619424.
These slides are provided under the Creative Commons Attribution Licence:
http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms.
www.laceproject.eu
@laceproject
9
10. Discussion 1: Please identify
• Main source of data
• Benefits of different kinds of data
• Drawbacks to getting access to such data
10
11. Discussion 2: Please agree
What data are you willing to share, with
whom and under what conditions?
11
12. Get involved!
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Editor's Notes
This is a simple version of a title slide for a LACE presentation.
This is a simple version of a title slide for a LACE presentation.
This is the default concluding slide, with information about the speaker(s) and rights for the slide (if a Creative Commons licence has been granted)