The document discusses integrating the qualitative data analysis software ATLAS.ti into advanced qualitative research methods courses. It was introduced as a project management tool beyond just data analysis. Students were required to use ATLAS.ti to submit assignments throughout various courses. This provided early exposure and supported continued use. Suggestions included providing adequate access and technical support, balancing methodological and technical instruction, and creating meaningful assignments that allowed students to pursue their own research topics.
Improving Research Productivity of Science Technology & Engineering Students ...Felipe De Oca
The document summarizes a study that evaluated the use of a learning management system (LMS) using Google web-based software among science, technology, and engineering students. The study used a concurrent mixed methods design to collect quantitative data through questionnaires and qualitative data through interviews and focus groups. The results showed that students were highly satisfied with the LMS and found it easy to use. Analysis of students' research manuscripts that were collaboratively developed using the LMS showed high quality in content, organization, and format. Students reported that the LMS enabled real-time collaboration beyond the classroom and helped them successfully complete and win awards for their research projects. In conclusion, the LMS was effective in facilitating collaboration, monitoring, and feedback on students
Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews He...IJECEIAES
This document discusses topic discovery in online course reviews using LDA (Latent Dirichlet Allocation) while leveraging review helpfulness. It begins by introducing MOOCs (Massive Open Online Courses) and the role of reviews in understanding the teaching-learning process. The authors collect reviews from Class-Central and analyze review features like ratings, lengths, and common words. They classify reviews as helpful or unhelpful using a Naive Bayes model trained on review votes. Finally, the authors propose applying LDA to helpful reviews to discover influential topics discussed by learners related to courses, like "learn easy excellent class program". Results show the proposed method improves LDA perplexity scores. The goal is to understand learner experiences through uncovered topics in
Designing Learning Analytics for Humans with Humansalywise
The document discusses designing learning analytics tools with a human-centered approach by involving intended users. It notes that past learning analytics have focused more on technical systems than human ones. Only a small percentage of tools reported user needs analysis or usability testing. This can result in tools being misaligned with user needs and perceptions, undermining trust. The presentation describes NYU's learning analytics work which aims to build partnerships putting people first. It discusses initial design processes, fieldwork examining instructor analytics use, and implications for tool redesign and implementation supports to better facilitate pedagogical decision-making.
This document summarizes several projects and resources related to learning analytics. It discusses the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) project at the University of Edinburgh which aims to develop critical and participatory approaches to educational data analysis. It also mentions the Learning Analytics Report Card (LARC) project which explores critical awareness with report cards. Additionally, it provides an overview of the Supporting Higher Education to Integrate Learning Analytics (SHEILA) project which developed a learning analytics policy framework through interviews and surveys. The document also shares findings from the SHEILA project about the adoption of learning analytics in higher education and key challenges identified. It outlines the principles and purposes of the University of Edinburgh's
STEM Teaching Tools: Resources for equitable science teaching and learningSERC at Carleton College
This webinar provided an overview of STEM Teaching Tools, a collection of professional learning resources to support equitable 3D instruction aligned with the Next Generation Science Standards (NGSS). Deb Morrison from the University of Washington presented on the tools, which were co-designed by educators and researchers to help teachers implement formative assessment and inquiry-based teaching practices. The tools have been widely used and have expanded access to professional development resources. Upcoming events from the organizers were also announced.
Learning Analytics Dashboards for Advisors – A Systematic Literature ReviewIJCI JOURNAL
Learning Analytics Dashboard for Advisors is designed to provide data-driven insights and visualizations to support advisors in their decision-making regarding student academic progress, engagement, targeted support, and overall success. This study explores the current state of the art in learning analytics dashboards, focusing on specific requirements for advisors. By examining existing literature and case studies, this research investigates the key features and functionalities essential for an effective learning analytics dashboard tailored to advisor needs. This study also aims to provide a comprehensive understanding of the landscape of learning analytics dashboards for advisors, offering insights into the advancements, opportunities, and challenges in their development by synthesizing the current trends from a total of 21 research papers used for analysis. The findings will contribute to the design and implementation of new features in learning analytics dashboards that empower advisors to provide proactive and individualized support, ultimately fostering student retention and academic success.
Improving Research Productivity of Science Technology & Engineering Students ...Felipe De Oca
The document summarizes a study that evaluated the use of a learning management system (LMS) using Google web-based software among science, technology, and engineering students. The study used a concurrent mixed methods design to collect quantitative data through questionnaires and qualitative data through interviews and focus groups. The results showed that students were highly satisfied with the LMS and found it easy to use. Analysis of students' research manuscripts that were collaboratively developed using the LMS showed high quality in content, organization, and format. Students reported that the LMS enabled real-time collaboration beyond the classroom and helped them successfully complete and win awards for their research projects. In conclusion, the LMS was effective in facilitating collaboration, monitoring, and feedback on students
Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews He...IJECEIAES
This document discusses topic discovery in online course reviews using LDA (Latent Dirichlet Allocation) while leveraging review helpfulness. It begins by introducing MOOCs (Massive Open Online Courses) and the role of reviews in understanding the teaching-learning process. The authors collect reviews from Class-Central and analyze review features like ratings, lengths, and common words. They classify reviews as helpful or unhelpful using a Naive Bayes model trained on review votes. Finally, the authors propose applying LDA to helpful reviews to discover influential topics discussed by learners related to courses, like "learn easy excellent class program". Results show the proposed method improves LDA perplexity scores. The goal is to understand learner experiences through uncovered topics in
Designing Learning Analytics for Humans with Humansalywise
The document discusses designing learning analytics tools with a human-centered approach by involving intended users. It notes that past learning analytics have focused more on technical systems than human ones. Only a small percentage of tools reported user needs analysis or usability testing. This can result in tools being misaligned with user needs and perceptions, undermining trust. The presentation describes NYU's learning analytics work which aims to build partnerships putting people first. It discusses initial design processes, fieldwork examining instructor analytics use, and implications for tool redesign and implementation supports to better facilitate pedagogical decision-making.
This document summarizes several projects and resources related to learning analytics. It discusses the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) project at the University of Edinburgh which aims to develop critical and participatory approaches to educational data analysis. It also mentions the Learning Analytics Report Card (LARC) project which explores critical awareness with report cards. Additionally, it provides an overview of the Supporting Higher Education to Integrate Learning Analytics (SHEILA) project which developed a learning analytics policy framework through interviews and surveys. The document also shares findings from the SHEILA project about the adoption of learning analytics in higher education and key challenges identified. It outlines the principles and purposes of the University of Edinburgh's
STEM Teaching Tools: Resources for equitable science teaching and learningSERC at Carleton College
This webinar provided an overview of STEM Teaching Tools, a collection of professional learning resources to support equitable 3D instruction aligned with the Next Generation Science Standards (NGSS). Deb Morrison from the University of Washington presented on the tools, which were co-designed by educators and researchers to help teachers implement formative assessment and inquiry-based teaching practices. The tools have been widely used and have expanded access to professional development resources. Upcoming events from the organizers were also announced.
Learning Analytics Dashboards for Advisors – A Systematic Literature ReviewIJCI JOURNAL
Learning Analytics Dashboard for Advisors is designed to provide data-driven insights and visualizations to support advisors in their decision-making regarding student academic progress, engagement, targeted support, and overall success. This study explores the current state of the art in learning analytics dashboards, focusing on specific requirements for advisors. By examining existing literature and case studies, this research investigates the key features and functionalities essential for an effective learning analytics dashboard tailored to advisor needs. This study also aims to provide a comprehensive understanding of the landscape of learning analytics dashboards for advisors, offering insights into the advancements, opportunities, and challenges in their development by synthesizing the current trends from a total of 21 research papers used for analysis. The findings will contribute to the design and implementation of new features in learning analytics dashboards that empower advisors to provide proactive and individualized support, ultimately fostering student retention and academic success.
This document summarizes a presentation on learning analytics in MOOCs given at a data science and social research conference. It defines key terms like learning analytics and discusses challenges like the interdisciplinary nature of the field and its current state of infancy. It also examines how learning analytics can help with issues in MOOCs like effectiveness, business models, technology/pedagogy, and more. The EMMA project framework for learning analytics in MOOCs is presented, including its use of dashboards, the XAPI standard, and clustering/network analysis of learner data. Conclusions discuss pedagogical neutrality and future work.
TOWARDS A MULTI-FEATURE ENABLED APPROACH FOR OPTIMIZED EXPERT SEEKINGcsandit
With the enormous growth of data, retrieving information from the Web became more desirable
and even more challenging because of the Big Data issues (e.g. noise, corruption, bad
quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a
topic, has been a real concern in the last 15 years. This kind of task comes in handy when
building scientific committees, requiring to identify the scholars’ experience to assign them the
most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with
plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In
this paper, we propose an expert seeking approach with specifying the most desirable features
(i.e. criteria on which researcher’s evaluation is done) along with their estimation techniques.
We utilized some machine learning techniques in our system and we aim at verifying the
effectiveness of incorporating influential features that go beyond publications
Issues and challenges in learning atla sti among graduate studentsMerlien Institute
This document summarizes an ATLAS.ti training workshop for graduate students. The workshop aimed to improve students' qualitative data analysis skills and familiarize them with ATLAS.ti software. 10 students participated in a 5-hour hands-on training using sample research data. Evaluations found that students gained basic ATLAS.ti knowledge but struggled with qualitative analysis concepts. Challenges included students' heavy quantitative backgrounds and not using their own research data.
Aligning Learning Analytics with Classroom Practices & NeedsSimon Knight
The Learning Analytics Research Network (LEARN) invites you to join us for a talk about the exciting ways in which the University of Technology Sydney is using participatory design to augment existing classroom practices with learning analytics. Simon Knight, a LEARN Visiting Scholar from the University of Technology Sydney, will introduce a variety of projects, including their work developing analytics to support student writing.
Come meet others at NYU interested in learning analytics while learning from the examples of leading work in Australia. A light lunch will be served and the talk will be followed by a short Q&A. RSVP is required.
About Simon Knight
Simon Knight is a lecturer at the University of Technology Sydney in the Faculty of Transdisciplinary Innovation. His research investigates how people find and evaluate evidence, particularly in the context of learning and educator practices. Dr Knight received his Bachelor’s degree in Philosophy and Psychology from the University of Leeds before completing a teacher education program and Philosophy of Education MA at the UCL Institute of Education. Following teaching high school social sciences, Dr Knight completed an MPhil in Educational Research Methods at Cambridge, and PhD in Learning Analytics at the UK Open University.
About Simon’s Talk
How do we make use of data about our students to support their learning, and where does learning analytics fit into that? Educators are increasingly asked to work with data and technologies such as learning analytics to support and provide evidence of student learning. However, what learning analytics developers should design for, and how educators will implement analytics, is unclear. Learning analytics risks the same levels of low uptake and implementation as many other educational technologies if they do not align with educator practice and needs. How then do we tackle this gap, to support and develop technologies that are implemented in practice, for impact on learning?
At the University of Technology Sydney, we have taken a participatory design based approach to designing and implementing learning analytics in practice, and understanding their impact. In our work we have identified existing practices with which learning analytics may be aligned to augment them. This talk introduces some of these projects, particularly drawing on our work in developing analytics to support student writing (writing analytics), giving examples of how analytics were aligned with existing pedagogic practices to support learning. Through this augmentation, supported by design-based approaches, we argue we can develop research and practice in tandem.
Learning Design and ResearchMethods/StatisticsJames Dalziel
This document discusses the potential for using learning design approaches in teaching research methods and statistics. It provides examples of how learning design has already been applied to teaching statistics collaboratively using learning activity sequences. There is potential to further apply learning design by having students use their own data, such as from surveys or labs, as the basis for statistical analysis activities to increase engagement. Future learning design systems could aim to integrate automated data collection and presentation of students' data back to them.
1. The document discusses the prospects for using learning analytics to achieve adaptive learning models. It describes adaptive learning and different levels of adaptive technologies, including platforms that react to individual user data and those that leverage aggregated data across users.
2. It outlines the pathway to achieving adaptive learning analytics, including using LMS analytics dashboards, predictive analytics, and adaptive learning analytics. Case studies and examples of existing applications are provided.
3. A proof of concept reference model for learning analytics is proposed, including a basic analytics process and an advanced process using predictive and adaptive algorithms. Linked open data for connecting curriculum standards and digital resources is also discussed.
MODULE HANDBOOK BA M4X01434Academic skills.docxaudeleypearl
MODULE HANDBOOK BA
M4X01434
Academic skills
LEVEL 4
SCHOOL OF BUSINESS, FINANCE AND MANAGEMENT
FACULTY OF BUSINESS AND MANAGEMENT
2017-2018
MODULE CODE: M4X01434
TITLE: Academic Skills
DATED: July 2016
LEVEL: 4
CREDITS 20
JACS CODE: N100
AIM(S)
The skills needed for higher education are ultimately gained through studying at that level; they evolve and mature through practice, trial and error, feedback from others and student reflection. This module aims to provide students with the underlying study/research strategies and software skills that can accelerate that learning process. Students will be encouraged to develop a reflective, active, positive approach to learning, and to take responsibility for their own learning. Such skills promote a deeper understanding of the topics studied throughout the programme; they support lifelong learning, and are the transferable skills desired in the employment context.
LEARNING OUTCOMES
Upon the successful completion of this module, the student should be able to demonstrate the ability to:
1. Analyse the published literature relating to a management related topic and produce a fully referenced management report
2. Design and deploy a range of primary data collection methods.
3. Evaluate and interpret qualitative and quantitative data and present the findings to specialist and non-specialist audiences
4. Evaluate the appropriateness of different approaches to information gathering.
INDICATIVE CONTENT
· Identifying skills (e.g. self-evaluation, skills needed for higher education, transferable/employment skills).
· Organising study (e.g. time management, organising space, organising resources)
· Gathering relevant information (e.g. effective note taking, using the library and the internet, reflecting on experience)
· Communicating and presenting information (e.g. presentation techniques, styles)
· Developing an appropriate writing style (e.g. planning and structuring essays and reports, linking ideas together, using facts, opinions or arguments, analytical thinking, etc.)
· Referencing convention (e.g. the Harvard System)
· Revision and examination techniques (e.g. preparation, organisation, memory aids, managing stress)
· Using computers and e-learning to support learning (e.g. the VLE, Internet search techniques)
· Key research skills/data collection methods (e.g. primary and secondary sources, interview, questionnaire, observation, focus groups, questionnaire design, sampling methods)
· Presentation of data using charts, diagrams and graphs.
· Measures of central tendency (mean, median, mode)
· Using word-processing software (e.g. creating tables, using a variety of document templates for reports, minutes, CVs etc., outline numbering, applying styles, automatic tables of contents, referencing, drawing and other toolbars)
· Using spreadsheet software (e.g. using formulae such as min, max, sum, autosum, autofill, function wizard, relative and absolute cell referencing.
MODULE HANDBOOK BA M4X01434Academic skills.docxroushhsiu
MODULE HANDBOOK BA
M4X01434
Academic skills
LEVEL 4
SCHOOL OF BUSINESS, FINANCE AND MANAGEMENT
FACULTY OF BUSINESS AND MANAGEMENT
2017-2018
MODULE CODE: M4X01434
TITLE: Academic Skills
DATED: July 2016
LEVEL: 4
CREDITS 20
JACS CODE: N100
AIM(S)
The skills needed for higher education are ultimately gained through studying at that level; they evolve and mature through practice, trial and error, feedback from others and student reflection. This module aims to provide students with the underlying study/research strategies and software skills that can accelerate that learning process. Students will be encouraged to develop a reflective, active, positive approach to learning, and to take responsibility for their own learning. Such skills promote a deeper understanding of the topics studied throughout the programme; they support lifelong learning, and are the transferable skills desired in the employment context.
LEARNING OUTCOMES
Upon the successful completion of this module, the student should be able to demonstrate the ability to:
1. Analyse the published literature relating to a management related topic and produce a fully referenced management report
2. Design and deploy a range of primary data collection methods.
3. Evaluate and interpret qualitative and quantitative data and present the findings to specialist and non-specialist audiences
4. Evaluate the appropriateness of different approaches to information gathering.
INDICATIVE CONTENT
· Identifying skills (e.g. self-evaluation, skills needed for higher education, transferable/employment skills).
· Organising study (e.g. time management, organising space, organising resources)
· Gathering relevant information (e.g. effective note taking, using the library and the internet, reflecting on experience)
· Communicating and presenting information (e.g. presentation techniques, styles)
· Developing an appropriate writing style (e.g. planning and structuring essays and reports, linking ideas together, using facts, opinions or arguments, analytical thinking, etc.)
· Referencing convention (e.g. the Harvard System)
· Revision and examination techniques (e.g. preparation, organisation, memory aids, managing stress)
· Using computers and e-learning to support learning (e.g. the VLE, Internet search techniques)
· Key research skills/data collection methods (e.g. primary and secondary sources, interview, questionnaire, observation, focus groups, questionnaire design, sampling methods)
· Presentation of data using charts, diagrams and graphs.
· Measures of central tendency (mean, median, mode)
· Using word-processing software (e.g. creating tables, using a variety of document templates for reports, minutes, CVs etc., outline numbering, applying styles, automatic tables of contents, referencing, drawing and other toolbars)
· Using spreadsheet software (e.g. using formulae such as min, max, sum, autosum, autofill, function wizard, relative and absolute cell referencing ...
Poster: Perspectives on Increasing Competency in Using Digital Practices and ...Katja Reuter, PhD
We believe that the quality and efficiency of all phases of the clinical and translational research (CTR) process can potentially be increased by using digital practices and tools in open and networked contexts. However, most CT researchers lack the training to take advantage of the benefits that the Internet and the social Web provide. Standardized training in digital practices and tools (Digital Scholarship) to conduct CTR has not been formalized through structured curriculum, learning approaches, and evaluation. Our overall goal is to develop a robust curriculum to train CTR researchers in digital scholarship. Here we present preliminary data from a qualitative study that describes the range of key stakeholders’ perspectives on the need to: (A) formalize educational efforts in digital scholarship among CTR trainees; and (B) develop an educational framework that defines core competencies, methods, and evaluation methods. Presented at Translational Science 2018 conference in Washington, DC on April 20, 2018.
Overview of C-SAP open educational resources projectCSAPOER
This presentation showcases, discusses and reflects upon the work of the C-SAP "Open Educational Resources" project. Our project, "Evaluating the Practice of Opening up Resources for Learning and Teaching in the Social Sciences", was part of a pilot programme (funded by the HEA and JISC), which sought to explore issues around the sharing of educational material from a disciplinary perspective. Whilst exploring, with our academic project partners, the principles and issues around releasing educational material (institutional, contractual, administrative), we have also sought to develop some insights into the processes of sharing practice, and look forward to discussing the findings in this forum.
C-SAP e-learning forum: Overview of Open Educational Resources projectCSAPSubjectCentre
The document summarizes the C-SAP Open Educational Resources project which funded 14 subject projects to develop and release open educational content. It discusses the rationale for open educational resources including encouraging sharing between institutions and universal sharing of materials. It describes the subject strands funded, challenges of sharing educational content, and a proposed toolkit and mapping process to contextualize modules and expose implicit pedagogical design for reuse.
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalChris Ballard
Speaking engagement at LACE SoLAR Flare hosted by the Open University. Turning Learning Analytics Research into Practice at Tribal. A video of my talk can be found at http://stadium.open.ac.uk/stadia/preview.php?whichevent=2606&s=1&schedule=3411&option=&record=0#
Sociology and anthropology briefings (C-SAP collections project)CSAPSubjectCentre
This literature review was written as part of the C-SAP (Higher Education Academy's Centre for Sociology, Anthropology and Politics) project "Discovering Collections of Social Science Open Educational Resources". The project ran from August 2010 - August 2011 as part of Phase 2 of the HEFCE-funded Open Educational Resources (OER) programme. The programme focused in particular on issues related to the discovery and use of OER by academics and was managed jointly by the Higher Education Academy [HEA] and Joint Information Systems Committee [JISC].
Q CHAPTER NINE (toc1.html#c09a)Qualitative Methods.docxmakdul
Q
CHAPTER NINE (toc1.html#c09a)
Qualitative Methods (toc1.html#c09a)
ualitative methods demonstrate a different approach to scholarly inquiry than methods of quantitative research.
Although the processes are similar, qualitative methods rely on text and image data, have unique steps in data
analysis, and draw on diverse designs. Writing a methods section for a proposal for qualitative research partly
requires educating readers as to the intent of qualitative research, mentioning specific designs, carefully reflecting on the
role the researcher plays in the study, drawing from an ever-expanding list of types of data sources, using specific protocols
for recording data, analyzing the information through multiple steps of analysis, and mentioning approaches for
documenting the accuracy—or validity—of the data collected. This chapter addresses these important components of
writing a good qualitative methods section into a proposal. Table 9.1
(http://content.thuzelearning.com/books/Creswell.7641.17.1/sections/c09#tab9.1) presents a checklist for reviewing the
qualitative methods section of your proposal to determine whether you have addressed important topics.
Table 9.1 (http://content.thuzelearning.com/books/Creswell.7641.17.1/sections/c09#tab9.1a) A Checklist of Questions
for Designing a Qualitative Procedure
_____________ Are the basic characteristics of qualitative studies mentioned?
_____________ Is the specific type of qualitative design to be used in the study mentioned? Is the history of, a definition
of, and applications for the design mentioned?
_____________ Does the reader gain an understanding of the researcher’s role in the study (past historical, social,
cultural experiences, personal connections to sites and people, steps in gaining entry, and sensitive
ethical issues) and how they may shape interpretations made in the study?
_____________ Is the purposeful sampling strategy for sites and individuals identified?
_____________ Are the specific forms of data collection mentioned and a rationale given for their use?
_____________ Are the procedures for recording information during the data collection detailed (such as protocols)?
_____________ Are the data analysis steps identified?
_____________ Is there evidence that the researcher has organized the data for analysis?
_____________ Has the researcher reviewed the data generally to obtain a sense of the information?
_____________ Has the researcher coded the data?
_____________ Have the codes been developed to form a description and/or to identify themes?
_____________ Are the themes interrelated to show a higher level of analysis and abstraction?
_____________ Are the ways that the data will be represented mentioned—such as in tables, graphs, and figures?
_____________ Have the bases for interpreting the analysis been specified (personal experiences, the literature,
questions, action agenda)?
https://content.ashford.edu/print/toc1.html#c09a
https://content.ashfo ...
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
This document proposes a model for automatically clustering Thai students' online homework assignments before teachers grade them. The model uses five parts: 1) Thai word segmentation, 2) stop-word removal, 3) term weighting, 4) document clustering using k-means, and 5) performance evaluation. The model was tested on 1,000 student assignments and achieved high accuracy, purity, and F-measure scores similar to human grading, allowing teachers to grade assignments more efficiently.
The document discusses a reference model for learning analytics with four dimensions:
1) Data and environments - what data is collected from what sources
2) Stakeholders - who are the people involved (learners, teachers, institutions)
3) Objectives - why is the data being analyzed (understanding learning, optimizing environments)
4) Methods - how the data is analyzed (data mining, statistics, visualization)
The document reviews related fields like academic analytics, educational data mining, and personalized learning, and proposes that learning analytics draws upon methods from these areas.
The document outlines a three tier model for promoting institutional adoption of learning analytics at universities.
Tier 1 involves small scale pilot projects using various learning analytics tools to provide insights. Tier 2 establishes a community of interest to share practices. Tier 3 develops learning analytics principles, frameworks and governance models for institutional implementation.
The model was applied at Victoria University of Wellington, resulting in learning analytics principles and framework documents, and progress towards an institutional governance model to bring analytics to scale safely while respecting data ethics. Various pilot projects provided lessons about the need for staff capability development and coordination across the university.
El documento describe la historia y propósito de los sellos verdes y ecoetiquetado. Los sellos verdes comenzaron en Alemania en 1978 para informar a los consumidores sobre productos menos dañinos para el medio ambiente. Ahora hay varios programas de sellos verdes en todo el mundo que buscan promover la producción y consumo sostenibles certificando productos que cumplen con estándares ambientales rigurosos.
Este documento presenta información sobre el microscopio, incluyendo su historia, partes, tipos, y cómo usarlo y cuidarlo correctamente. Explica que el microscopio ha permitido el descubrimiento de nuevos organismos y estructuras biológicas. Describe los componentes ópticos y mecánicos de un microscopio de luz, así como los pasos para preparar una muestra y enfocar usando objetivos de diferentes aumentos, incluyendo el objetivo de inmersión.
This document summarizes a presentation on learning analytics in MOOCs given at a data science and social research conference. It defines key terms like learning analytics and discusses challenges like the interdisciplinary nature of the field and its current state of infancy. It also examines how learning analytics can help with issues in MOOCs like effectiveness, business models, technology/pedagogy, and more. The EMMA project framework for learning analytics in MOOCs is presented, including its use of dashboards, the XAPI standard, and clustering/network analysis of learner data. Conclusions discuss pedagogical neutrality and future work.
TOWARDS A MULTI-FEATURE ENABLED APPROACH FOR OPTIMIZED EXPERT SEEKINGcsandit
With the enormous growth of data, retrieving information from the Web became more desirable
and even more challenging because of the Big Data issues (e.g. noise, corruption, bad
quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a
topic, has been a real concern in the last 15 years. This kind of task comes in handy when
building scientific committees, requiring to identify the scholars’ experience to assign them the
most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with
plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In
this paper, we propose an expert seeking approach with specifying the most desirable features
(i.e. criteria on which researcher’s evaluation is done) along with their estimation techniques.
We utilized some machine learning techniques in our system and we aim at verifying the
effectiveness of incorporating influential features that go beyond publications
Issues and challenges in learning atla sti among graduate studentsMerlien Institute
This document summarizes an ATLAS.ti training workshop for graduate students. The workshop aimed to improve students' qualitative data analysis skills and familiarize them with ATLAS.ti software. 10 students participated in a 5-hour hands-on training using sample research data. Evaluations found that students gained basic ATLAS.ti knowledge but struggled with qualitative analysis concepts. Challenges included students' heavy quantitative backgrounds and not using their own research data.
Aligning Learning Analytics with Classroom Practices & NeedsSimon Knight
The Learning Analytics Research Network (LEARN) invites you to join us for a talk about the exciting ways in which the University of Technology Sydney is using participatory design to augment existing classroom practices with learning analytics. Simon Knight, a LEARN Visiting Scholar from the University of Technology Sydney, will introduce a variety of projects, including their work developing analytics to support student writing.
Come meet others at NYU interested in learning analytics while learning from the examples of leading work in Australia. A light lunch will be served and the talk will be followed by a short Q&A. RSVP is required.
About Simon Knight
Simon Knight is a lecturer at the University of Technology Sydney in the Faculty of Transdisciplinary Innovation. His research investigates how people find and evaluate evidence, particularly in the context of learning and educator practices. Dr Knight received his Bachelor’s degree in Philosophy and Psychology from the University of Leeds before completing a teacher education program and Philosophy of Education MA at the UCL Institute of Education. Following teaching high school social sciences, Dr Knight completed an MPhil in Educational Research Methods at Cambridge, and PhD in Learning Analytics at the UK Open University.
About Simon’s Talk
How do we make use of data about our students to support their learning, and where does learning analytics fit into that? Educators are increasingly asked to work with data and technologies such as learning analytics to support and provide evidence of student learning. However, what learning analytics developers should design for, and how educators will implement analytics, is unclear. Learning analytics risks the same levels of low uptake and implementation as many other educational technologies if they do not align with educator practice and needs. How then do we tackle this gap, to support and develop technologies that are implemented in practice, for impact on learning?
At the University of Technology Sydney, we have taken a participatory design based approach to designing and implementing learning analytics in practice, and understanding their impact. In our work we have identified existing practices with which learning analytics may be aligned to augment them. This talk introduces some of these projects, particularly drawing on our work in developing analytics to support student writing (writing analytics), giving examples of how analytics were aligned with existing pedagogic practices to support learning. Through this augmentation, supported by design-based approaches, we argue we can develop research and practice in tandem.
Learning Design and ResearchMethods/StatisticsJames Dalziel
This document discusses the potential for using learning design approaches in teaching research methods and statistics. It provides examples of how learning design has already been applied to teaching statistics collaboratively using learning activity sequences. There is potential to further apply learning design by having students use their own data, such as from surveys or labs, as the basis for statistical analysis activities to increase engagement. Future learning design systems could aim to integrate automated data collection and presentation of students' data back to them.
1. The document discusses the prospects for using learning analytics to achieve adaptive learning models. It describes adaptive learning and different levels of adaptive technologies, including platforms that react to individual user data and those that leverage aggregated data across users.
2. It outlines the pathway to achieving adaptive learning analytics, including using LMS analytics dashboards, predictive analytics, and adaptive learning analytics. Case studies and examples of existing applications are provided.
3. A proof of concept reference model for learning analytics is proposed, including a basic analytics process and an advanced process using predictive and adaptive algorithms. Linked open data for connecting curriculum standards and digital resources is also discussed.
MODULE HANDBOOK BA M4X01434Academic skills.docxaudeleypearl
MODULE HANDBOOK BA
M4X01434
Academic skills
LEVEL 4
SCHOOL OF BUSINESS, FINANCE AND MANAGEMENT
FACULTY OF BUSINESS AND MANAGEMENT
2017-2018
MODULE CODE: M4X01434
TITLE: Academic Skills
DATED: July 2016
LEVEL: 4
CREDITS 20
JACS CODE: N100
AIM(S)
The skills needed for higher education are ultimately gained through studying at that level; they evolve and mature through practice, trial and error, feedback from others and student reflection. This module aims to provide students with the underlying study/research strategies and software skills that can accelerate that learning process. Students will be encouraged to develop a reflective, active, positive approach to learning, and to take responsibility for their own learning. Such skills promote a deeper understanding of the topics studied throughout the programme; they support lifelong learning, and are the transferable skills desired in the employment context.
LEARNING OUTCOMES
Upon the successful completion of this module, the student should be able to demonstrate the ability to:
1. Analyse the published literature relating to a management related topic and produce a fully referenced management report
2. Design and deploy a range of primary data collection methods.
3. Evaluate and interpret qualitative and quantitative data and present the findings to specialist and non-specialist audiences
4. Evaluate the appropriateness of different approaches to information gathering.
INDICATIVE CONTENT
· Identifying skills (e.g. self-evaluation, skills needed for higher education, transferable/employment skills).
· Organising study (e.g. time management, organising space, organising resources)
· Gathering relevant information (e.g. effective note taking, using the library and the internet, reflecting on experience)
· Communicating and presenting information (e.g. presentation techniques, styles)
· Developing an appropriate writing style (e.g. planning and structuring essays and reports, linking ideas together, using facts, opinions or arguments, analytical thinking, etc.)
· Referencing convention (e.g. the Harvard System)
· Revision and examination techniques (e.g. preparation, organisation, memory aids, managing stress)
· Using computers and e-learning to support learning (e.g. the VLE, Internet search techniques)
· Key research skills/data collection methods (e.g. primary and secondary sources, interview, questionnaire, observation, focus groups, questionnaire design, sampling methods)
· Presentation of data using charts, diagrams and graphs.
· Measures of central tendency (mean, median, mode)
· Using word-processing software (e.g. creating tables, using a variety of document templates for reports, minutes, CVs etc., outline numbering, applying styles, automatic tables of contents, referencing, drawing and other toolbars)
· Using spreadsheet software (e.g. using formulae such as min, max, sum, autosum, autofill, function wizard, relative and absolute cell referencing.
MODULE HANDBOOK BA M4X01434Academic skills.docxroushhsiu
MODULE HANDBOOK BA
M4X01434
Academic skills
LEVEL 4
SCHOOL OF BUSINESS, FINANCE AND MANAGEMENT
FACULTY OF BUSINESS AND MANAGEMENT
2017-2018
MODULE CODE: M4X01434
TITLE: Academic Skills
DATED: July 2016
LEVEL: 4
CREDITS 20
JACS CODE: N100
AIM(S)
The skills needed for higher education are ultimately gained through studying at that level; they evolve and mature through practice, trial and error, feedback from others and student reflection. This module aims to provide students with the underlying study/research strategies and software skills that can accelerate that learning process. Students will be encouraged to develop a reflective, active, positive approach to learning, and to take responsibility for their own learning. Such skills promote a deeper understanding of the topics studied throughout the programme; they support lifelong learning, and are the transferable skills desired in the employment context.
LEARNING OUTCOMES
Upon the successful completion of this module, the student should be able to demonstrate the ability to:
1. Analyse the published literature relating to a management related topic and produce a fully referenced management report
2. Design and deploy a range of primary data collection methods.
3. Evaluate and interpret qualitative and quantitative data and present the findings to specialist and non-specialist audiences
4. Evaluate the appropriateness of different approaches to information gathering.
INDICATIVE CONTENT
· Identifying skills (e.g. self-evaluation, skills needed for higher education, transferable/employment skills).
· Organising study (e.g. time management, organising space, organising resources)
· Gathering relevant information (e.g. effective note taking, using the library and the internet, reflecting on experience)
· Communicating and presenting information (e.g. presentation techniques, styles)
· Developing an appropriate writing style (e.g. planning and structuring essays and reports, linking ideas together, using facts, opinions or arguments, analytical thinking, etc.)
· Referencing convention (e.g. the Harvard System)
· Revision and examination techniques (e.g. preparation, organisation, memory aids, managing stress)
· Using computers and e-learning to support learning (e.g. the VLE, Internet search techniques)
· Key research skills/data collection methods (e.g. primary and secondary sources, interview, questionnaire, observation, focus groups, questionnaire design, sampling methods)
· Presentation of data using charts, diagrams and graphs.
· Measures of central tendency (mean, median, mode)
· Using word-processing software (e.g. creating tables, using a variety of document templates for reports, minutes, CVs etc., outline numbering, applying styles, automatic tables of contents, referencing, drawing and other toolbars)
· Using spreadsheet software (e.g. using formulae such as min, max, sum, autosum, autofill, function wizard, relative and absolute cell referencing ...
Poster: Perspectives on Increasing Competency in Using Digital Practices and ...Katja Reuter, PhD
We believe that the quality and efficiency of all phases of the clinical and translational research (CTR) process can potentially be increased by using digital practices and tools in open and networked contexts. However, most CT researchers lack the training to take advantage of the benefits that the Internet and the social Web provide. Standardized training in digital practices and tools (Digital Scholarship) to conduct CTR has not been formalized through structured curriculum, learning approaches, and evaluation. Our overall goal is to develop a robust curriculum to train CTR researchers in digital scholarship. Here we present preliminary data from a qualitative study that describes the range of key stakeholders’ perspectives on the need to: (A) formalize educational efforts in digital scholarship among CTR trainees; and (B) develop an educational framework that defines core competencies, methods, and evaluation methods. Presented at Translational Science 2018 conference in Washington, DC on April 20, 2018.
Overview of C-SAP open educational resources projectCSAPOER
This presentation showcases, discusses and reflects upon the work of the C-SAP "Open Educational Resources" project. Our project, "Evaluating the Practice of Opening up Resources for Learning and Teaching in the Social Sciences", was part of a pilot programme (funded by the HEA and JISC), which sought to explore issues around the sharing of educational material from a disciplinary perspective. Whilst exploring, with our academic project partners, the principles and issues around releasing educational material (institutional, contractual, administrative), we have also sought to develop some insights into the processes of sharing practice, and look forward to discussing the findings in this forum.
C-SAP e-learning forum: Overview of Open Educational Resources projectCSAPSubjectCentre
The document summarizes the C-SAP Open Educational Resources project which funded 14 subject projects to develop and release open educational content. It discusses the rationale for open educational resources including encouraging sharing between institutions and universal sharing of materials. It describes the subject strands funded, challenges of sharing educational content, and a proposed toolkit and mapping process to contextualize modules and expose implicit pedagogical design for reuse.
SoLAR Flare 2015 - Turning Learning Analytics Research into Practice at TribalChris Ballard
Speaking engagement at LACE SoLAR Flare hosted by the Open University. Turning Learning Analytics Research into Practice at Tribal. A video of my talk can be found at http://stadium.open.ac.uk/stadia/preview.php?whichevent=2606&s=1&schedule=3411&option=&record=0#
Sociology and anthropology briefings (C-SAP collections project)CSAPSubjectCentre
This literature review was written as part of the C-SAP (Higher Education Academy's Centre for Sociology, Anthropology and Politics) project "Discovering Collections of Social Science Open Educational Resources". The project ran from August 2010 - August 2011 as part of Phase 2 of the HEFCE-funded Open Educational Resources (OER) programme. The programme focused in particular on issues related to the discovery and use of OER by academics and was managed jointly by the Higher Education Academy [HEA] and Joint Information Systems Committee [JISC].
Q CHAPTER NINE (toc1.html#c09a)Qualitative Methods.docxmakdul
Q
CHAPTER NINE (toc1.html#c09a)
Qualitative Methods (toc1.html#c09a)
ualitative methods demonstrate a different approach to scholarly inquiry than methods of quantitative research.
Although the processes are similar, qualitative methods rely on text and image data, have unique steps in data
analysis, and draw on diverse designs. Writing a methods section for a proposal for qualitative research partly
requires educating readers as to the intent of qualitative research, mentioning specific designs, carefully reflecting on the
role the researcher plays in the study, drawing from an ever-expanding list of types of data sources, using specific protocols
for recording data, analyzing the information through multiple steps of analysis, and mentioning approaches for
documenting the accuracy—or validity—of the data collected. This chapter addresses these important components of
writing a good qualitative methods section into a proposal. Table 9.1
(http://content.thuzelearning.com/books/Creswell.7641.17.1/sections/c09#tab9.1) presents a checklist for reviewing the
qualitative methods section of your proposal to determine whether you have addressed important topics.
Table 9.1 (http://content.thuzelearning.com/books/Creswell.7641.17.1/sections/c09#tab9.1a) A Checklist of Questions
for Designing a Qualitative Procedure
_____________ Are the basic characteristics of qualitative studies mentioned?
_____________ Is the specific type of qualitative design to be used in the study mentioned? Is the history of, a definition
of, and applications for the design mentioned?
_____________ Does the reader gain an understanding of the researcher’s role in the study (past historical, social,
cultural experiences, personal connections to sites and people, steps in gaining entry, and sensitive
ethical issues) and how they may shape interpretations made in the study?
_____________ Is the purposeful sampling strategy for sites and individuals identified?
_____________ Are the specific forms of data collection mentioned and a rationale given for their use?
_____________ Are the procedures for recording information during the data collection detailed (such as protocols)?
_____________ Are the data analysis steps identified?
_____________ Is there evidence that the researcher has organized the data for analysis?
_____________ Has the researcher reviewed the data generally to obtain a sense of the information?
_____________ Has the researcher coded the data?
_____________ Have the codes been developed to form a description and/or to identify themes?
_____________ Are the themes interrelated to show a higher level of analysis and abstraction?
_____________ Are the ways that the data will be represented mentioned—such as in tables, graphs, and figures?
_____________ Have the bases for interpreting the analysis been specified (personal experiences, the literature,
questions, action agenda)?
https://content.ashford.edu/print/toc1.html#c09a
https://content.ashfo ...
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
This document proposes a model for automatically clustering Thai students' online homework assignments before teachers grade them. The model uses five parts: 1) Thai word segmentation, 2) stop-word removal, 3) term weighting, 4) document clustering using k-means, and 5) performance evaluation. The model was tested on 1,000 student assignments and achieved high accuracy, purity, and F-measure scores similar to human grading, allowing teachers to grade assignments more efficiently.
The document discusses a reference model for learning analytics with four dimensions:
1) Data and environments - what data is collected from what sources
2) Stakeholders - who are the people involved (learners, teachers, institutions)
3) Objectives - why is the data being analyzed (understanding learning, optimizing environments)
4) Methods - how the data is analyzed (data mining, statistics, visualization)
The document reviews related fields like academic analytics, educational data mining, and personalized learning, and proposes that learning analytics draws upon methods from these areas.
The document outlines a three tier model for promoting institutional adoption of learning analytics at universities.
Tier 1 involves small scale pilot projects using various learning analytics tools to provide insights. Tier 2 establishes a community of interest to share practices. Tier 3 develops learning analytics principles, frameworks and governance models for institutional implementation.
The model was applied at Victoria University of Wellington, resulting in learning analytics principles and framework documents, and progress towards an institutional governance model to bring analytics to scale safely while respecting data ethics. Various pilot projects provided lessons about the need for staff capability development and coordination across the university.
El documento describe la historia y propósito de los sellos verdes y ecoetiquetado. Los sellos verdes comenzaron en Alemania en 1978 para informar a los consumidores sobre productos menos dañinos para el medio ambiente. Ahora hay varios programas de sellos verdes en todo el mundo que buscan promover la producción y consumo sostenibles certificando productos que cumplen con estándares ambientales rigurosos.
Este documento presenta información sobre el microscopio, incluyendo su historia, partes, tipos, y cómo usarlo y cuidarlo correctamente. Explica que el microscopio ha permitido el descubrimiento de nuevos organismos y estructuras biológicas. Describe los componentes ópticos y mecánicos de un microscopio de luz, así como los pasos para preparar una muestra y enfocar usando objetivos de diferentes aumentos, incluyendo el objetivo de inmersión.
El documento introduce los principios básicos de la microscopía óptica e histología. Explica los diferentes tipos de microscopios como campo claro, campo oscuro, contraste de fase y fluorescencia, así como sus componentes y usos. También describe las técnicas para preparar y teñir muestras histológicas para su análisis microscópico.
Este documento presenta instrucciones para el uso y manejo del microscopio en el laboratorio de biología. Describe las partes del microscopio y cómo usarlas correctamente para observar muestras a diferentes aumentos magnificación. También cubre técnicas como el montaje húmedo y la tinción para mejorar la visualización de las muestras. El objetivo es capacitar a los estudiantes en el uso básico del microscopio, una herramienta fundamental en biología.
Este documento presenta las normas de bioseguridad que deben seguirse en el laboratorio de biología. Describe los riesgos físicos, químicos y biológicos y las medidas para prevenirlos, como el uso de batas, guantes, tapabocas y protección ocular. También incluye instrucciones sobre el manejo seguro de reactivos, el uso de mecheros y hornos, y la limpieza de derrames. El objetivo es minimizar los riesgos y crear un ambiente de trabajo seguro.
Este documento presenta una guía de laboratorio de biología para el primer semestre. Introduce el microscopio y su importancia para el descubrimiento de las células. Explica los tipos de microscopios, incluyendo microscopios simples y compuestos, y describe sus partes como el objetivo, ocular, platina y tubo. También cubre normas de seguridad en el laboratorio y materiales necesarios para las prácticas.
Este documento presenta una introducción a la microbiología ambiental y biología ambiental, así como protocolos para realizar observaciones y recuentos microbiológicos en el laboratorio. Explica los componentes básicos del microscopio óptico y cómo manejarlo correctamente. Luego proporciona instrucciones detalladas para llevar a cabo diversas prácticas de laboratorio como la observación de células, bacterias, protozoos y hongos, y el recuento de varios microorganismos ambientales.
1) La ecología humana estudia la interacción entre los seres humanos y su entorno, incluyendo los sistemas culturales, socioeconómicos y biológicos.
2) El ecosistema humano se compone de poblaciones humanas, el entorno geográfico y biológico, y el entorno cultural.
3) El entorno cultural actúa como un tampón entre las poblaciones humanas y los entornos geográfico y biológico, y puede modular o amplificar los estímulos ambientales que afectan a
Este documento presenta la información sobre la asignatura optativa de Ecología Humana del grado en Ciencias Ambientales de la Universidad Autónoma de Madrid. La asignatura cubre temas como la evolución humana, el comportamiento eco-evolutivo, la historia ecológica de la humanidad desde los cazadores-recolectores hasta las sociedades industriales modernas, y la ecología urbana. Incluye bloques temáticos, contenidos de cada bloque y excursiones de trabajo de campo relacionadas con la asignatura.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
8.Isolation of pure cultures and preservation of cultures.pdf
19_paulus_bennett_4424.pdf
1. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Teaching Qualitative Research Methods With ATLAS.ti: Beyond Data Analysis
Trena Paulus, Ann Bennett
Abstract
This presentation will share best practices for integrating ATLAS.ti into advanced qualitative research methods
courses. During the spring, summer and the current fall 2013 semesters, students were required to use ATLAS.ti as a
project management tool for their semester’s work in order to develop the skills they would need to continue its use
during the thesis phase of their programs. In these courses students are typically engaged in independent field work
projects, in which they are reviewing the literature, collecting data, transcribing, and/or engaging in data analysis.
Each of these phases were conducted within ATLAS.ti and shared with the instructor at regular intervals throughout
the semester for feedback. By introducing ATLAS.ti during coursework, positioning it as a project management tool
in addition to a data analysis tool, and supporting students’ early experiences with its use, we anticipate that these
novice researchers will be more likely to continue using the tool to support their work. Suggestions for best practice
for this instructional approach will include a focus on how to: provide adequate access and technical support, bal
ance methodological and technical instruction, create meaningful student assignments, and provide effective feed
back.
Keywords
ATLAS.ti, teaching, advanced qualitative methods course, data analysis, best practice, technical support,
methodological instruction, technical instruction, students, data management tool, reviewing literature
Introduction
Davidson and di Gregorio (2011) noted in the most recent fourth edition of The Sage Handbook of
Qualitative Research that “most senior researchers in the field of qualitative research, and many rising re
searchers, still lack exposure to QDAS use in their graduate training” (p. 635). At the same time, mem
bers of the “digital native” millennial generation are more comfortable than ever with pervasive comput
ing environments and, in our experience, are actively seeking ways to use the technologies for their re
search. These factors and other considerations have led to our integration of one QDAS tool, ATLAS.ti,
into our advanced qualitative research courses in an effort to frame the tool as not just a data analysis
tool but a project management tool. This reframing follows the lead of Muhr (1997) who referred to AT
LAS.ti as “the knowledge workbench”, Konopasek’s (2008) description of ATLAS.ti as a “textual laborat
ory” and diGregorio and Davidson’s (2008) conceptualization of QDAS tools as supporting “e-projects”
as a research design.
While misunderstandings, skepticism and distrust of QDAS persist for a variety of reasons (Davidson & di
Gregorio, 2011), any new technology does indeed challenge the way things have traditionally been
done. Rogers’ (2003) diffusion of innovations theory argued that how the new innovation is communic
ated to people is important and suggested that both early adopters and resisters can be highly influential
to those who remain undecided. In the case of qualitative research, it is possible that the way that the es
tablished scholars in the field, including methods instructors, introduce novice scholars to new technolo
gies may shape how they adopt the tools in their own practice. For this reason we decided to make the
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2. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
use of QDAS “the norm” in our qualitative research courses by providing support and “demystification”
of their features.
QDAS tools, we argue, have the potential to increase reflexive and ethical practices, transparency of
choices and collaboration during the research process (Paulus, Lester & Dempster, forthcoming). Our
goal for requiring the use of ATLAS.ti as part of coursework, and as a project management tool, was to
increase student comfort level with ATLAS.ti early in their research careers, to build a network of users on
our campus, and demonstrate its utility beyond data analysis for their dissertation and future research
work. In this paper we report on the introduction of ATLAS.ti at our university and its subsequent integ
ration as a required component of three advanced qualitative research courses. Through a reflective prac
tice approach, we describe our course design decisions, outcomes, and “best practices” for integrating
QDAS tools into qualitative methods courses.
Context
Since 2008 our university has offered a 15 semester hour graduate certificate in qualitative research
methods (coordinated by first author Paulus), most of which are taught by faculty in the Department of
Educational Psychology and Counseling as service courses for our College. In January 2013 our university
acquired a site license for ATLAS.ti along with 20 hours of dedicated graduate assistant support for qual
itative research (second author Bennett) funded by the university’s Office of Information Technology Re
search Computing division. Bennett provides workshops, class visits and individual consultations for is
sues related to qualitative research design, including the use of ATLAS.ti. These circumstances made it
feasible for the first time to begin to integrate ATLAS.ti into our advanced qualitative methods courses.
(While we provide an overview of ATLAS.ti in the introductory qualitative methods course, students are
not required to use it for their work.)
Semester Class Number Course assignments
Spring 2013 Advanced qualitative
research methods in
14 • Project proposal
• Two progress reports
Education • Final report
• Individual literature review
• and/or analyzed data
Summer 2013 Digital tools for 18 • Two skill builder reports
qualitative research • Methods section of proposal
Fall 2013 Discourse analysis in 16 • Mini lit review
Educational
environments
• Transcribing audio/video files
• Data analysis throughout
semester
Table 1: Description of courses
In the spring 2013 Advanced Qualitative Research, students created their own individual project proposal
for the course, a project in which they were required to demonstrate both methodological competence
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3. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
(generally by completing a methodological literature review) and data analysis competence (generally by
analyzing pilot study data.) During this first semester of integrating ATLAS.ti, students were required to
submit one HU several times throughout the semester. The HU contained their individual project propos
al, two interim progress reports and the final project report, as illustrated in Figure 1.
The instructor provided feedback as memos and comments on each iteration of the HU as illustrated in
Figure 2.
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Figure 1: Spring 2013 Assignments Submitted as an HU.
4. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
The goal of this requirement was for students to become comfortable with and proficient in creating an
HU, adding primary documents, reading comments and memos, and bundling and unbundling the HU
for feedback. ATLAS.ti as a project management tool can be used to document decisions, adding trans
parency to research work, work in teams for a more collaborative approach, and engage in reflexivity
through writing regular reflective memos. Students were encouraged, but not required, to create a
second HU in which to conduct their methodological literature reviews and/data analysis projects, and
most did so, further developing their proficiency with the tool as they neared the dissertation phase of
their work. See Figure 3.
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Figure 2: Instructor feedback and student response as a memo.
5. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
In summer 2013 the students in Digital Tools for Qualitative Research were also required to use ATLAS.ti
(some for the second time) as a project management tool to submit their required assignments for in
structor feedback. These assignments included two “skill builder” activities in which students chose two
tools (e.g.citation management software, transcription software, data analysis software) to master.
Nearly all of the students chose to focus on a particular use of ATLAS.ti (for literature reviews, transcrip
tion, or data analysis) as part of these skill builders. See Figure 4.
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Figure 3: Student project entering field notes as new text document, coding and memoing data.
6. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
This semester, fall 2013, students in Discourse Analysis in Educational Environments, some of which have
also taken the previous two courses, are being required to use ATLAS.t. Rather than as a project manage
ment tool to submit course assignments, however, all students will conduct a mini-literature review (of 5
sources), transcribe an audio or video file and analyze their data within ATLAS.ti. Because, unlike the pre
vious two courses, this course is focused on one particular type of analysis, the students can conduct
their analysis in the tool and receive instructor feedback on it.
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Figure 4: Summer 2013 Assignments submitted as an HU.
7. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
In addition to what has been described thus far, students in all three courses were/are required to engage
in reflective journaling. As reflexivity is a key part of qualitative research (Watt, 2007), students were
asked to reflect regularly on course readings and their learning experiences on individual blogs, and these
reflections were valuable as formative evaluation data points throughout the course. These blog posts,
all course materials, instructor communications and instructor reflections are currently being systematic
ally analyzed in order to compile best practices for the use of CAQDAS tools for teaching qualitative
methods. This initial paper reports on our reflections, as instructors, on the course design, outcomes and
tentative best practices that emerged from these first iterations of ATLAS.ti integration.
Best Practices
Provide adequate access and technical support.
We found it important to let students know about the pre-requisite skills and tools they would need to
be successful well in advance of the course. For example, students needed access to a PC machine with
administrator privileges. Since a good number of our students are Apple/Mac users, this issue required
individual consultations well in advance of the start of the course about how best to access the program.
(A set of netbooks had been purchased by our department for students to check out specifically for this
purpose.)
Students also needed to be proficient in the use of the cloud-based shared folder system Dropbox (or
Google Drive) for sharing HUs with the instructor. The instructor set up a shared folder for the class with
subfolders for each student (see Figure 5). Students were to bundle their HU and put it in the folder,
after which the instructor would move it onto her computer for feedback, then rebundle and put it back
in the folder. This helped keep student work organized and accessible throughout the course.
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8. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Students were also instructed to have the site license downloaded and successfully launched prior to the
class. All of this preparatory work was important so as not to spend valuable class time on trouble-shoot
ing technical difficulties. Without Ann’s support as the graduate assistant dedicated to qualitative re
search design it is unlikely our attempts to integrate ATLAS.ti into our courses would have been success
ful. She held introductory training sessions prior to the start of each semester, and for students who
could not attend we directed them to webinars, tutorials and/or an individual consultations to get every
one up to speed before the course even began.
Balance Methodological And Technical Instruction
We felt it was extremely important for the ATLAS.ti component of the course to not overshadow the
primary focus of the class on developing methodological competence, though some may argue that the
two are becoming increasingly intertwined. While the Digital Tools course was obviously focused more
on technology, even that course (and the accompanying textbook) is situated primarily in the phases of
the qualitative research process, before moving on to discussion of new tools that could support it.
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Figure 5: Shared Google Drive for assignment submission
9. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
While at least one class session each semester was dedicated to an overview of ATLAS.ti in the context of
what would be expected in that particular course, student support for using the tool typically took place
as individual consultations outside of class with Ann. In this way course time could be spent on issues of
methodology and not just technology. Individual consultations were mostly on topics related to setting
up the HU (organization) and basic functions of ATLAS (coding). Most of the students wanted to be
sure everything they needed for the class was placed in the HU correctly. Many inquired about families
and how they worked, the difference between supercodes and families, and how to create and modify
families. Once the students received HUs back from the instructor, a few students needed assistance ac
cessing comments and memos or how to reply to them.
One of the biggest challenges was helping students understand how to bundle, share and unbundle their
work. This was particularly challenging because the instructor stores her own work on the shared univer
sity server, which makes the files more difficult for students to recognize (see Figure 6).
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Figure 6: Unpack copy bundle error message create student confusion.
10. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Editor''comment: Finding it difficult to unpack a copy bundle file, is an issue that also other user express. Therefore,
Susanne took this paper as an opportunity to create a video tutorial on packing and unpacking copy bundle files.
You can view it here: http://www.youtube.com/watch?v=s6Ah3MUohY0&list=UUmHxeU4wVDyqJBZ4UosU13A
Finally, an important strategy for ensuring that the focus did not stray from methodology solely to tech
nology was to reassure the students that if they got too overwhelmed learning ATLAS.ti, they could opt
out of this requirement. This reassurance seemed to provide enough comfort that it prevented anyone
from taking this option or becoming so wrapped up in the functionality of ATLAS.ti that the quality of
their learning suffered.
Create Meaningful Student Assignments
We have found that course assignments in advanced qualitative research courses ideally provide the flex
ibility for students to pursue their own research agendas and make progress
towards their dissertation. This is important at our institution because students come to these courses
from a variety of program areas (sport studies, teacher education, nutrition, communications, educational
leadership, English, business) and with a variety of preferred research approaches (ethnography, phe
nomenology, case study, etc.) Historically, Advanced Qualitative Research Methods has been geared to
ward students working on their dissertation proposals or engaging in pilot studies. Thus we limited the
requirements for using ATLAS.ti in the spring 2013 course to submitting project proposals and progress
reports and learning to become comfortable with ATLAS.ti as a project management tool. Thus we were
pleasantly surprised that a good number of students did take the opportunity to learn ATLAS.ti beyond
what was required.
In the spring 2013 course a practice HU was created and shared with the students to use as a “play
ground” in which to demonstrate basic functionality (adding primary documents, memos, codes, net
work views, user accounts, bundling) as well as allow students to become comfortable manipulating an
HU (see Figure 7). Soon, however, students who had their own data preferred to work with their own
HU rather than the practice HU. Others learned to use the transcribing features and still others experi
mented with conducting their literature review in an HU.
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11. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Peer workshops are a major component of the Advanced Qualitative Research course during which stu
dents engage in peer feedback, collaborative analysis and discussion. In the spring, one student group
tried to engage in collaborative analysis using ATLAS.ti, but we did not realize this soon enough to help
them create their HU in a way that would allow successful merging. In the future we will build in collab
orative analysis as part of the course in order to demonstrate how to set up an HU that is ready for
teamwork. We also feel that incorporating more peer feedback and workshop opportunities across
courses will be a good next step.
In the summer 2013 Digital Tools course more ATLAS.ti features were introduced since the purpose of
the course was to learn new tools. More extensive demonstrations and workshops took place around re
viewing the literature, collecting data (field notes), coding/memoing/creating networks, transcribing, dir
ect image/audio/video analysis, and even importing survey data. Students had the choice, however, to
focus on two tools for the purpose of their skill builder assignments. Nearly every student chose to mas
ter some aspect of ATLAS.ti for at least one of their assignments. These included using ATLAS.ti for con
ducting a literature review, transcribing an audio file, and analyzing a data set. These were submitted as
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Figure 7: Practice HU created for Spring 2013 class.
12. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
bundled files and reviewed by the instructor, who could then make comments and suggestions as to their
efficiency and effectiveness with the tool.
Discussing ATLAS.ti and other new tools in the context of affordances and constraints is particularly im
portant to keep the focus on methodology rather than only technology. Clearly there are tradeoffs
whenever new tools are adopted, and the ability to discuss these in an informed manner can go a long
way towards encouraging the adoption of the best tools at the appropriate time. The methods proposal
paper in the Digital Tools class required students to make a case for which tools they would use to con
duct their proposed research. By learning how to provide a justification for the use of a tool, including its
affordances and constraints, students were preparing themselves to talk in an informed manner with
their committee members who may not be as familiar with the tools or understand how QDAS could
contribute to the transparent, reflexive and collaborative nature of their study (see Figure 8).
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Figure 8:
13. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Students in this summer’s Digital Tools course recommended that ATLAS.ti be incorporated even more
thoroughly throughout the course. They suggested that we require all students to complete an entire
project from start to finish within ATLAS.ti. This suggestion is being implemented as part of this fall’s Dis
course Analysis class, as it is easier to do this when all students are using the same research methodology
and doing the same kind of analysis assignments.
Provide Effective Feedback.
In all classes, students regularly submitted HUs as bundled files uploaded to Dropbox for instructor re
view. This modeled the use of ATLAS.ti as a collaborative tool – both in terms of project management
and, as will be the case for the fall 2013 Discourse Analysis courses, for data analysis. By creating user
accounts, it becomes clear who has contributed what to the HU (see Figure 9). By using comments and
memos to provide feedback and engage in conversation with the students and their work, ATLAS.ti is
positioned as a tool that affords the ability for researchers to transparently document their work and
share it with others. This kind of collaboration was not really possible prior to the development of QDAS
tools, and is one of their great strengths.
Using ATLAS.ti in this way, however, requires extensive use of the families and comments features to
keep the HU organized. More attention is needed in future classes to convey to students the importance
of organizing the HU in a way that someone else could understand. For example, deciding when to use
primary document families and when to start a new HU was often a difficult one for students to make
without having extensive experience with how the software works.
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Figure 9: Author names reflect which team member created memos.
14. TEACHING QUALITATIVE RESEARCH METHODS WITH ATLAS.TI: BEYOND DATA ANALYSIS
Throughout, however, our feedback was intended to convey that students should view the ATLAS.ti re
quirement as an opportunity to learn a QDAS tool in a safe space. Coursework is a lower-stakes environ
ment than the dissertation itself, and with instructor and dedicated graduate assistant support, this is an
ideal environment in which to invest the time in learning a tool that will have a great payoff down the
road. We believe that early research experiences are crucial, with analytic methods and processes being
put into place that become more difficult to change later. Thus, learning how to use a QDAS tool early
on, we hope, will result not only in continued use of the tool but the ability to pick up other tools later
and collaborate with researchers in a more transparent and reflexive way.
Conclusions
Integrating ATLAS.ti into the coursework has resulted in some challenges. Not all senior faculty whose
students take our courses are comfortable with QDAS tools and some careful navigation of questions
and concerns has been necessary to avoid alienating our colleagues. By introducing ATLAS.ti during
coursework rather than at the dissertation phase, we are attempting to position it as a project manage
ment tool rather than solely a data analysis tool and to demystify its functionality and uses. This has al
lowed features other than data analysis, such as the report writing through memos and transcription
tools, to be highlighted and explored in a safe and supported space. By supporting students’ early experi
ences with its use, we anticipate that they may be more likely to continue using the tool to support their
work.
Based on feedback from the summer course, in this fall’s discourse analysis class we are also moving bey
ond submitting project reports to requiring a mini-literature review and discourse analysis of data to take
place within ATLAS.ti. This will be possible in large part because approximately one third of the students
were in previous classes and are familiar with the tool and will be able to support their peers. To prepare
students for the challenges of teamwork, this fall we will not only provide instructor feedback, but have
students engage in peer feedback as well.
In order to effectively support students learning ATLAS.ti, we cannot emphasize enough the importance
of having robust technical support and training, as instructors will likely not have the time to
troubleshoot 15 or more students learning the tool for the first time. Having support available also re
flects the institution’s commitment to the value of the tool, which encourages more people to attempt to
learn it, and allows instructors to focus on issues of methodology rather than technology.
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Trena Paulus
University of Tennessee. 515BEC, Department of Educational Psychology & Counseling, 1122, Volunteer Boulevard,
University of Tennessee, Knoxville, TN 37996
Ann Bennett
University of Tennessee
Article Information
This article is published at the Digital Repository of Technische Universität Berlin, URN urn:nbn:de:kobv:83-opus4-
44240, http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-44240. It is part of ATLAS.ti User Conference 2013 :
Fostering Dialog on Qualitative Methods, edited by Susanne Friese and Thomas Ringmayr. Berlin: Universitätsverlag
der TU Berlin, 2014, ISBN 978-3-7983-2692-7 (composite publication), URN urn:nbn:de:kobv:83-opus4-51577,
http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-51577
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