I recently listened to a talk given by Dave Snowden through the MOOC course, Change.MOOC.ca. Dave is a well spoken man with passion and conviction about cognitive processes. My understanding of the theory he follows is that humans react to cause and effect situations through simplistic, complicated, complex or chaotic thinking drawing on environmental cues, cultural nuances and/or past experiences while always dealing with uncertainty. And he states we react and think differently depending on our predisposition and past experience where our “styles of creativity produce different patterns of behaviour.” I can’t attempt to summarize his work unless I read it more thoroughly.

During his presentation he implied that formal education stifles creativity and innovative thought – one reason he did not pursue a PhD. Having studied the higher education field and spending most of my adult life pursuing formal degrees I have to agree with him somewhat, and I’ll address that in a minute. However, I become cautious when statements are made that negate a particular system to propose another. In my graduate studies we were taught to balance all ideas to build our argument. Detesting something is more an emotional response than an intellectual one. In that way, formal education did provide me with a balanced outlook among other skills.

My sense of Dave’s point about creativity was that freely exploring ideas, visions, and perhaps passionate thoughts would most likely need to take place outside of academia. I think he is right based on my personal experience. In my formal education, I was quite aware of the hoops I had to jump through (requirements, restrictions, supervisory advice and committee approval, and even journal submissions) when developing my work, whether a paper, exam, or dissertation. All through my studies I tried to keep a part of myself and thinking that was mine, though difficult at times. I remember my doctoral supervisor advising me to keep the exploration of ‘risky’ theses for a time when my degree was complete and to see the formal program as a place to learn the methods of deep study and critical thinking. This made sense and I appreciated the advice.

Today, I read and explore as I wish and develop my own thoughts; however, there is a trade-off  – applying it to sensible things, like paid work. Again, I’ve learned to balance my idealism with pragmatism to apply fresh concepts to client’s educational needs. And sometimes clients want their educational products developed into more conventional forms, which I do.

A recent post about informal learning, called the Accidental Learner, nicely supports Dave’s perspective and reflects how we learn outside the formal setting.

Here is how I develop innovative thinking and creativity:

Recently I completed an instructional development project for a client who was open to my concept of designing an educational diploma program in the same manner as radio programming. Their student body were dispersed across a wide non-urban region, and were working adults with limited transportation seeking to update their education and skills.

My radio programming concept derived from the need for flexible delivery for this student population. More specifically, I enjoy listening to CBC radio via my computer, iPhone, iPad, and car radio. The CBC website offers a transparent schedule of different radio channels and programs to be streamed live, through recorded versions, and subscriptions to podcasts. They also offer interesting background information on programs, musicians, etc. Basically, they provide a variety of choices catering to the preferences of listeners, very much like the varied learning styles and needs of learners.

Transposing the concept of radio programing to educational programs or courses would entail providing a selection of content, experiences, delivery formats, and locations for students to choose. Next, I describe the basic concepts of my instructional design idea.

Course or Module Design

• Each course or module would have multiple, separate, non-linear units delivered in flexible formats
• All units would need to be completed to obtain credit for the course
• Half of the course/module units would be are self-paced and delivered online with support from a distant resource person (preferably an instructor or SME)
• The remaining units would be short (3-4 weeks) instructor-led seminars and workshops  delivered on a continual basis (i.e. every few months)
• The instructor-led units could be conducted through f2f sessions at a campus/institution location, remote learning centres, and/or through video and audio conferencing
• If for formal purposes, each course with its many units could equate to 40+ hours of instructional time, whether in-person, virtual or simulated
• Students could challenge courses or practicums by submitting specific items, or be awarded credit for any part of the program or course through PLAR investigations

Course or Module Unit Design

To gain credit for a course or module students would need to obtain credit for each unit, which could be taken at anytime and simultaneously with other course units. The units within each course would include the following important learning components:

• Introduction: background information delivered online with a resource person in a self-paced manner; includes text materials along with tutorials, simulations and/or quizzes
• Seminar: short instructor-led seminars to explore theoretical concepts
• Workshop: short instructor-led workshops focusing on practical application
• Practical: self-paced projects to gain experience in real-world situations
• Assessment: submit completed assignments from introduction, seminar, workshop and practical components; assessed by instructor (follows the model by University of the People)

Illustration of Course Design
(Click image for larger view)

Such a program or course design would require an online administration system that allows students to register for courses and course units as well as track their credits and progress. The systems would also initiate delivery of learning material, whether shipped or downloadable, and access to online LMS or other platforms. A recommender and alert system would also be advantageous to keep students progressing through the multi-modular, unidirectional program or course.

Such an instructional design rests on a number of popular learning theories and approaches, such as for adult learners, experiential learning, active and authentic learning, constructivism, and  self-directed learning (I have discussed my ID approach in another blog entry).

Dave Cormier spoke this week via the change MOOC venue on rhizomatic learning – a model meaning that “curriculum is not driven by predefined inputs from experts; it is constructed and negotiated in real time by the contributions of those engaged in the learning process” (Cormier, 2008). I can appreciate Dave’s attempt to define and theorize the learning taking place in a networked, information-heavy world to illicit it and to  present an argument that formal education should apply it, as well.The distributed knowledge/network learning/chaos theory movement, as played out through MOOCs, is a good attempt to forward new ways of learning due to the affordances of technology and the learning hunger of people.

Yet, I can’t help feeling that measures are taking place to ‘name’ what is happening to justify it as the BIG way to learn while diminishing traditional formal ways of learning. To me, this is an attempt to throw the baby out with the bathwater and not learn from the earlier work of others (okay, that might be a bit harsh – he does draw on constructivist type learning, etc.). Emerging as modern day critical theorists, Dave et al. are showing how traditional modes of learning are antiquated and ineffective. I do think there is truth to that but I also see the amazing work some teachers are doing within their imposed structures.

However, my concern is not that they are exploring new horizons (which I am following with them) but that the slate is being wiped clean in order to make way for another suggested mode of learning. If they can mix in the old by considering other foundational thinking and theory, I can buy in more. It takes time to think through these things and I appreciate how Dave, Stephen and George are being open and sharing in their thought process.

I rarely criticize others in a public venue but Dave has something and has my interest. So, Dave, my criticism is a good thing and I encourage you to keep developing your ideas and I will, too!

At the link below are slides for a recent workshop at the Community Access Symposium in Edmonton, Alberta.

In the slides, I outline some main theoretical frameworks and approaches I follow to design learning using technology. These frameworks and approaches inform and support the design strategies I use to create engaging blended or online learning. I also provide some basic e-learning tools and instructional design tools courtesy of Grainne Conole.

Workshop Slides

(http://www.slideshare.net/kedmonds/from-theory-to-tools-a-workshop-on-designing-blended-and-online-learning-9994692)

Note: the Excel tool for determining course e-learning elements by Conole and revised by Edmonds is here:  Course Dimensions E-Learning Design Tool

I have been following the latest MOOC - change11 topic on mobile learning, which intrigues me as a potential learning technology. It seems countries outside of North America are relying more on this technology for education, leaving us well behind. For instance, Africa is seeing an explosion of cell use: Did You Know – Mobile stats for Africa 2011 [video] providing potential to deliver distance education to mass amounts of people.

Yesterday,  Zoraini Wati Abas spoke on a mobile learning project at Open University Malaysia (bless her heart – it was midnight for her). The project consisted of sending text messages (SMS) to students at the university taking blended courses. Below are the different ways SMS was used to motivate and connect students. This is a slide from Zoraini’s presentation at: http://change.mooc.ca/week02.htm

Other mobile learning projects are more involved (lots happening ‘down under’), such as using smart phones to collect data by taking pictures or recording short videos. Two projects outlining more involved uses of mobile phones are:

• Smartphones give you wings: Pedagogical affordances of mobile Web 2.0 at Unitec New Zealand (2010)
• see Figure 1 of tools used on page 4
• A Design Requirements Framework for Mobile Learning Environments, Massey University, NZ (2007!)
• four m-learning projects are presented with one relying on infra red communication

Ethical issues are arising for mobile learning which needs to be considered before implementing. Not unlike other communication technologies, smart phones can be used for the wrong reasons such as bullying, spam, etc. Australia is addressing this issue head on. For instance, UNISA in South Australia has rules on using SMS at their institution:

• Guidelines on Electronic Communications with Students
• see the section: Communications through the SMS notification tool

As well, recently federal ministries in Australia are implementing a ‘digital social contract’ to handle offensive behaviours in cyberspace (Thanks for the link, Tim Winklemans): http://www.futuregov.asia/articles/2011/sep/18/au-digital-social-contract-online-behaviour/

I am excited about the potential of mobile learning to reach many willing students globally. I think this trend is going to develop more and more. Following those using this delivery technique outside of North American will be key.

Stephen Downes, George Siemens and Dave Cormier are at it again – delivering another freely accessible massive open online course about “Being connected changes learning.” This is my third attempt at joining one of their MOOC and have the following comments:

• I appreciate all the time they have put into creating the MOOCs and developing a large, international learning community
• as an independent scholar my world can be lonely pursing self-study – accessing this community is very helpful
• the line up of speakers is impressive focusing on current and emerging topics on connection, networks and e-learning
• they have refined the delivery of the MOOC, opportunity for participant input, and provide suggestions for following, interacting and reflecting on the copious amounts of information.  See: http://change.mooc.ca/how.htm

Consider joining this ride and take as much or as little from it as you need. Many people are generously giving to this efforts.

The course homepage: http://change.mooc.ca/index.html

Recently the US Department of Education shared their recommendations for improved learning through the use of technology in their report, Transforming American Education: Learning Powered by Technology. One section of the report I found interesting was on assessing learning.

Fueled by a statement from President Obama in 2009, the Office of Educational Technology took to task the need to assess 21st century skills in diagnostic and summative ways using technology. Note, they intentionally  moved away from computerized adaptive testing to have students learn, perform and be assessed on more complex skills, such as problem solving, critical thinking, entrepreneurship, and creativity.

Here are some of their suggestions:

1. Using simulated environments to assess problem solving, understanding sequence of events, and modelling complex reasoning skills (author’s note: while highly desirable, finding copyright-free products is difficult, and creating or buying such simulations is quite expensive; however, more is being developed in this area).
2. Use virtual environments to present student designs, graphing of results, running of tests, and recording data.
3. Assess and reward learning outside of class. The report gives an example of a competition where students must collect and synthesize information and apply knowledge all the while assessed through feedback delivered through technology.
4. Diagnose learning often during a course through survey questions, response devices, and then peer instruction. As well, have groups add handwritten notes or illustrations from tablets to a e-whiteboard and explain about their work. Another interesting technology idea is an assessment tool that provides support through hints and tutoring where the amount of support used shows areas of difficulty for the student. Last collecting student’s results, activities and how they learn can be compiled into a ‘playlist’ of customized learning activities
5. Valuable feedback on student understanding and ideas can be provided through social networks and online learning communities. Posting their work online, such as poems or videos, could solicit feedback from viewers and experts. A rubric would be needed to outline to reviewers what criteria are important.
6. Develop an assessment framework that assesses the following learning outcomes across collective work by students: collaboration, critical thinking, oral and written communication, technology use, and citizenship. E-portfolios and self-assessments could provide works to assess on this broader scale.
7. Last, gather the assessment data into aggregated and accessible forms by all, including the student. As well, link assessment data to needed teaching and learning resources.

Author’s note: this report helped me determine ways to use technology to assess higher learning skills. While some can be performed without technology, using tools can provide more complex learning environments as well as connections to a larger learning community and allow more diverse presentations, thus catering to different learning styles.

I usually develop curriculum to ask students to perform more complex skills. I am known to deter from creating quizzes and tests as I find them less effective to assess learning, but I do see the value of self-assessment of concepts and problem solving.

Rather I develop curriculum to include assignments and projects that show student’s thinking, development and new ideas. However, the marking of  essays, blogs, e-portfolios, presentations etc. is laborious for teachers and instructors. One suggestion given above is to assess 21st century skills across a diversity of student work rather than one piece at a time. This is something to consider.

Any input from my readers would be appreciated. How do you assess higher order thinking skills in efficient and effective ways?

Copyright by renjith krishnan

I had the oddest yet most vivid dream the other day. In my dream my husband was reading a novel and he asked me if I heard a noise coming from the speakers in the other room. We both went into the other room and noticed when he opened the book a sound started of birds chirping. It seemed his book gave off sound effects when he opened it. We thought that was neat.

Then, in my dream, the same happened to me when I opened a newspaper. To make hear sound we needed to have our wireless connection active and our stereo speakers on. I decided to patent the idea.

 

Upon awakening and recalling this episode I began to think this was an interesting concept – to have a multi-sensory experience when reading. Though somewhat like a singing greeting card (the ones that are embarrassing to open in the store) the sound effects when reading would be more subtle – almost out of ear range. For instance, there could be the soft sound of a heartbeat during an exciting excerpt, or the wind rustling when reading about nature, etc.

 

Technically, it would require inserting a very small microchip-like apparatus into the pages that looped a sound file. The technology is already there – it’s a matter of manufacturing it.

 

I have decided not to patent it, but if you ever experience a singing newspaper or book in the future, think of me.

I have been mulling over the use of learning analytics as a research method to determine if and how students are learning when engaged with online learning tools. At the latest Learning Analytics and Knowledge conference in Banff, Alberta, Canada, it occurred to me that one of the most troubling concepts for presenters was to determine what to measure when applying learning analytics. I agreed when some presenters stated merely measuring the number of log-ins, hits or postings did not provide an accurate indication of whether students were learning. For instance, in online learning environments (formal and informal) I don’t log on that much. When I do I download or link readings/materials and learn on my own more rather than with others. Once I feel I have a grasp of the content or ideas I log back on and engage somewhat near the end of the timeframe alloted. I learn in social and collaborative settings, but not as much as lauded by those promoting the idea.

The point is I don’t believe we can measure learning based on online activity alone – we need to include and bridge it with assessment. More important, learning is believed to be recognizable by a change in behaviour, thinking and attitude.; in short, we change and grow through learning and this needs to be captured, measured and examined to determine the success of students.

Capturing data to measure change in learners poses some problems. Ideally having pre- and post-tests would provide one way to measure change and growth, but it’s not possible to structure all learning activities or assessments that way. Other possible ways to measure learning is the level of achievement through various assessments, and through feedback from students on perceptions of their learning (via surveys, etc.). However, perceptions and knowledge acquisition are not synonymous. Determining evidence of  change in learners to use in learning analytics will be challenging but necessary in order to gather essential data.

Aside from evidence of change and growth, online learner interaction and engagement would provide data on their actions that perhaps attributed to their learning. I would suggest to move beyond using tracking data provided in a LMS and also include interactions in external learning environments and tools, such as blogs, wikis or social networks sites. I envision this data would include  frequency statistics (hits, posts) and network visualizations (connections).

Lastly, it would be important to determine the resources learners are accessing. It has been realized in higher education that instructors are not the sole holder of knowledge, and the net provides endless sources of materials, resources and expertise. This might be more difficult to determine, but perhaps reviewing the cited works within students’ work might provide the resources they used outside those offered in the class.

Triangulating this data will be essential to understand how students engaged online and how that contributed to their learning and growth. Examining the correlations of this data would indicate if and which online activities and resource use affected learning and which did not.

This is a rudimentary understanding of the use of learning analytics in education. I am about to engage in a research study where I will attempt to apply this analysis method. Hopefully over time I can develop my thinking and share new ideas about this topic along with my experiences from using learning analytics as an educational research method.

Recently I attended a conference in beautiful Banff, Alberta  on The First International Conference on Learning Analytics and Knowledge 2011 (LAK11), hosted by Tekri a division of Athabasca University. I was inspired by the people I met and learned from. I met peers such as George Siemens, Dave Cormier, Terry Anderson, Rory McGreal, Grainne Conole, and David Wiley. Additionally, I met a number of others who were statisticians, data analysts, programmers, educators, and innovators.

Below are my collective notes on the subjects discussed at the conference on the emerging research and analysis method, Learning Analytics. Please note this is an emerging concept and the notes reflect the array of perceptions and ideas by a wide range of people. It reflects the newness and potential for this process. (Please correct me if I misunderstood any notions.)

In subsequent posts I will provide my views on the method and its potential, challenges, and discrepancies in hopes to contribute to a scholarly discussion.

Notes:

• Overview
  • Learning analytics is analysing triangulated data (mashed-up data) about learners and their context, learning, and environment. It’s strategic analytics for administration, academics and context.
  • It is about attempting to map student comprehension within a specific domain. It draws on pragmatic analysis within the marketing field and analysis of consumer behaviour. The steps for learning analytics are collecting, reformatting or coding, cleansing and analysing (visually as well) the data. It is a form of data mining for representative information of learners and analysing it in automated ways.
  • The semantic web is a way to connect personal information about someone and determine patterns of behaving, knowing, and learning. However this requires bringing meaning to written words and creating ontologies to connect them. It’s a daunting task with the challenge of misinterpreted and subjective meaning. Ontologies could include those for content structure and type, user models, learning design, and domains.

• • Learning Analytics Goals
    • Mapping learner interactions and actions using multiple ontologies and theories
    • Testing learning theories and approaches
    • Choosing analysis types
      • Best to do predictive and optimization analysis versus descriptive and forecasting (Davenport & Harris)
    • Monitoring learning stages
      • Tracking student records and successes from high school to postsecondary
    • Determining learner support needed
      • Determine interventions to help students, recommenders, intelligent tutors
    • Revealing student patterns
      • Student course and resource choices (web tracking)
      • Association between media and roles
      • Student attention
      • PLE usage
      • Impact of group and collaborative work; communal view
    • Providing gap analysis:
      • Student progress compared to learning outcomes ontologies
    • Reviewing learning Outcomes
      • Analysis of 21^st century skills
      • Richness of dialogue (Mercer)
      • Student-rated discussion posts
      • Correlate online activity with grades
      • Correlate resources use with grades
    • Socio-technical affordances: technology, self and others
    • Effects of networks on workload, role, and learning
    • New directions:
      • New light or perspective on a phenomenon
      • New or altered predictive or causal models
      • New views on learning
  • Learning predictors:
    • 21^st century skills
    • granularity of learning
    • comprehension
    • student behaviour
    • expert vs novice behaviour
    • learning outcomes
    • interaction with others
    • participation patterns (SNA)
    • social isolation
    • use of learning tools/widgets
    • time on task
    • resource use
    • assessments of others on student work; community opinion
    • quality of discourse
    • effects of keynote speaker on discourse
    • effects of media on interaction
  • Learning environments analyzed:
    • Social networking sites
    • PLEs
    • LMS
    • Web2.0 tools
    • Laptops
  • Data sets:
    • hash tag used and connections (topic of interest and connections)
    • delicious tag used and connections (need semantic meanings)
    • twitter count of a url
    • student records/successes from high school to postsecondary
    • connection of student learning to other data (vendors, etc.)
    • discourse analysis using semantics, connections, and types of discourse (Mercer)
    • patterns of actions and behaviours to implement interventions
    • descriptive data: log in count and times, resources chosen
    • triangulation of website, course, library and online activity
    • social connections of learners
    • nested model: cluster behavioural data with social practices; combine quantitative data
    • merge interests, social connections, and resource use (how people learn)
    • dimensions involved in learning object use
    • contextualized action model: acts dependent upon another; semantic connection
    • merging concept analysis with social analysis
    • merging content analysis with topic modelling
    • institutional data for cross sectional and cross tabulation analysis
    • group analytics
    • automated discourse analysis

    Data analysis tools and methods

    • • ROLE  Responsive Open learning environment – tracking of widget use
      • Recommenders: based on resources used, learning actions, etc.
      • Contextualized Attention Metadata (CAM): track online movements
      • Google Analytics: website traffic
      • Google Social: public connections of web users
      • Screenscrape: reformatted published data from the web in HTML format
      • Any data sets in usable formats: CVS or Excel data
      • Yahoo Pipes: filter web content into site and analyze; data needs reformatting
      • Many Eyes: IBM visualisation tools; existing data sets and visualisations available
      • Gaffy mapping tool?
      • Google Refine: cleanses data
      • Freebase: data sets to mash-up; restructure to another model
      • Stanford Data Wrangler: restructures any unstructured data
      • bit.ly: analysis of connected web links
      • Python: API for Google Analytics to parse reports
      • Various dashboards of visualisations
      • Selection of shared metrics and algorithms
      • Gaffe: links among networks
      • Google Trends: traffic insight on favourite websites
      • SNAPP: social network visualisation
      • Lecture capture: analysis of viewer actions
      • Latent semantic analysis (LSA)/discourse analysis and interaction analysis
      • Social network analysis (SNA): visualization of social relationships in a network; analysis of nodes and ties
      • Game theory: capture and predict behaviours in strategic situations; dependency on others
      • Virtual machines tracking student actions; full computer systems
      • Wakoopa: track website visits
      • RescueTime: tracks time spent on tasks
      • LMS analysis: student activity and grades tracked in learning management systems
      • AAT (Athabasca U): new software, academic analysis tool for Moodle and any LMS; student engagement, tool use and outcomes
    • Questions about Learning Analytics
      • Who accesses and can access data?
      • Who needs the analysis?
      • Who knows about learning analytics?
      • What are the theories or models underlying analysis query?
      • How will others react to the analysis?
      • What new lens/perspective can be applied?
      • What minimal data is needed?
      • How use the results?
      • What interventions would be best?
      • What is learning and how measure it?
    • Challenges
      • Ability of data to show learning
      • Defining learning and learning ties
      • Defining drop out for online learners (these 3 are my interests)
      • Gathering and analysing data from multiple platforms
      • Developing infrastructures to collect, analyse, interpret and report data
        • Placement and access of infrastructure
        • Data management and technology
        • Student and faculty centred tools; useable interfaces
      • Valid and reliable data
        • Fragmented data on online learners
        • Quality of data
        • Working with unstructured data
        • Better data sets
        • Operational definitions of constructs
          • Expert opinion
          • Theory based
          • Data driven
        • Defining context in word meaning
        • Analyze causal rather than predictive data
        • Invisible network of participation
      • Security and privacy issues; being transparent
      • Dynamic vs. static analysis models; predictors constantly changing
      • Cultural considerations/differences
      • Automating discourse analysis
      • Merging big data (quantitative) and qualitative data: triangulation
    • Data Science Team Members
      • Stakeholders/users
      • Data scientists
      • Programmers
      • Statisticians
      • Visualiser
      • Learning scientists/instructional designers
      • Data evaluators
      • Information technologists
      • Interpreters
      • Project manager