• #Part 1: Theoretical Perspectives#

  • #Part 2: Research Method#

  • #Part 3: Data Collection Techniques#

  • #Part 4: Data Analysis Approach#

  • There are key philosophical assumptions that underlay the integrated components of this research study. Engeström’s (2001, 2011) Activity theory and Park’s (2011) merging of Activity Theory with Transactional Distance Theory (Moore, 1997) lies at the heart of relating Vygotsky’s key semiotic tool (that of language) to a digitally mediated representation of his zone of proximal development (ZPD) (Engeström, 1987, 2001; Fallon, 2011; Motteram, 2013; Vygotsky, 1978, 1994). I adopt Pena-Lopez’s (2012) developmental model of the blurred boundary and interface between formal educational Learning Management Systems (LMS)/Virtual Learning Environment (VLE) and the individual learner’s Personal Learning Environment (PLE)/Personal Knowledge Management System (PKMS) (see also Atwell, 2009, 2011). Finally, I further adapt this model to the experiences of the ELL/NNS in the English mediated HE context. It is within this English mediated context that I will attempt to generate survey data that tracks ELL/NNS strategic substitution and switching patterns as they access and utilize a continuum of formal, open and informal learning resources and also address to what degree these substitution patterns happen in their L1 and/or L2. The study will further attempt to analyse the sub processes that account for the degree ELL/NNS a) further develop linguistic, communicative and academic competencies; and b) the processes by which they successfully (or unsuccessfully) complete requisite formative and summative academic tasks and assessments.

  • [formal learning] LMS/VLE  ZPD  PLE/PKMS [informal learning]
    Analytics interface
    Digitally mediated learning resources:
    ELL/NNS substitution/switching patterns (L1 + L2)

    So, the core question is:

    What switching and substitution practices on the continuum of formal to informal multimodal learning resources do graduate ELLs/NNS use to complete the diverse types of coursework and summative tasks and assessments in a UK HE institution?

  • Lea and Jones (2010) offer a critique much of current research in mLearning, elearning and digitally enhanced learning. They cite a lack of theoretical modeling in research studies on learning and technology. These findings are similar to Viberg and Grolund (2012) meta-analysis of mLearning studies from 2007-2012. The theoretical framework presented in this study attempts to run a critical path (a red thread) throughout the full developmental cycle of the study (see theoretical framework outlined above). So what methodology will generate the data to address the core question?
    The Use of an Analytics Approach
    EDUCAUSE (2013) posits a working definition of analytics as “the use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues.” However, practical applications suggest the addition of “prediction”, “strategic” and “decision-making”. As a model to understand ELL strategic choices in regulating their linguistic, communicative and academic development analytics serves as a useful framework of analysis. It was established earlier the emphasis of this study is process over product with respect to the socially situated ELL experience in HE. A focus group of educational practitioners describe analytics as

    1. starting with a strategic question
    2. finding or collecting the appropriate data to answer that question
    3. analyzing the data with an eye toward prediction and insight
    4. representing or presenting findings in ways that are both understandable and actionable, and
    5. feeding back into the process of addressing strategic questions and creating new ones (p.6).
      The advent of digitally mediated learning environments has created an opportunity in education to digitally track learning transactions and automatically obtain repeatable data in HE. It is the actionable use of this repeatable analytics process that gives this kind of research advantages over traditional of forms analysis and reporting (ibid:7). It is in effect actionable research.
  • ELLs must repeatedly observe, orient, decide and act on (OODA):

    1) What do I need to know?
    2) What do I need to do?
    3) How do I do it? I need a strategy
    a) Analytics Interface - ongoing personal diagnostics feedback/feedforward based on MKO mediation and peer interactions/activities that feed into an ongoing LMS/PLE negotiated learning processes that they must self-monitor, self-evaluate and act to the best of their ability.
    b) Navigational Road Map
    4) Do it! Execute the plan (coping strategies)
    5) How did I do and how can I do better?

    Adapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23

  • To this end I will endeavor to generate several sets of data:
    Core Goal
    Data Set A: Core Goal of this Study – Learning Resource Substitution Patterns
    Sub Goals
    Data Set B: Establishing ELL Digital Profiles
    Data Set C: Learning Strategies for Syllabus Task Completion Scenarios
    To achieve these core goals, more specific sub-processes of this study include:
     Collection of data on the attitudes, perceptions, and usage of various devices and modes
     Collection of data on NNS accessing which digitally mediated learning resources in their L1 and/or L2
     Attitudes on help or hindrance to productivity and positive/negative perceptions of impact on student workload
     Student evaluation on effectiveness of a) devices b) applications (Education/Pedagogy 2.0)
     Collection of data on the attitudes, perceptions and practices of NNS interaction and engagement with VLE/LMS (i.e. Moodle)

  • [formal learning] LMS/VLE  ZPD  PLE/PKMS [informal learning]
    Analytics interface
    Digitally mediated learning resources:
    ELL/NNS substitution/switching patterns (L1 + L2)

    So, the core question is:

    What switching and substitution practices on the continuum of formal to informal multimodal learning resources do graduate ELLs/NNS use to complete the diverse types of coursework and summative tasks and assessments in a UK HE institution?

  • Such switching strategies will give empirical evidence as to what happens in the digitally mediated interface between Personal Learning Environments (PLE) and Formal Educational Learning Management Systems (LMS)/VLEs.

    To this end, I will endeavor to generate several data sets:
    Core Goal
    Data Set A: Core Goal of this Study – Learning Resource Substitution Patterns
    Sub Goals
    Data Set B: Determining Student (ELL) Digital Profiles
    Data Set C: Learning Strategies for Syllabus Task Completion Scenarios
    The Primacy of the Analytics Interface for the ELL
    One of the major goals of this study is to better understand how students employ strategies to navigate a) prescribed road maps (scaffolded) b) personally constructed road maps employed to access the necessary learning resources to achieve academic success. The navigational road map focuses learner objectives and outcomes mediated through their own PLE. What demands further research is how the NNS/ELL plans for and navigates English and digitally mediated academic environments through strategic choices that are both socially and culturally situated. These strategic choices are further formally or informally structured around five fundamental learner objectives and outcomes present in any given situated learning scenario:

  • Box 1: The Simplified ELL/NSS Situated Learning Framework

    ELLs must repeatedly observe, orient, decide and act on (OODA):

    1. What do I need to know?
    2. What do I need to do?
    3. How do I do it? I need a schema
      3.1. Analytics Interface
      3.2. Navigational Road Map
    4. Do it! Execute the plan (coping strategies)
    5. How did I do and how can I do better?

    Adapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23

  • These five rather general objectives and outcomes, and in rather pragmatic terms, are representative of the ELL experience in a HE learning environment. The analytics interface is that blurred boundary where the institutions learning management interface, the MKO (the instructors, mentors, tutors) and the personal learning environment of ELL all coalesce to construct new learner knowledge and enhanced academic literacy. The mediations that feedback and forward analytical data to the ELL/NSS must process are complex and unless appropriately modeled, scaffolded and applied, can by quite convoluted, confusing and disorienting. Zimmerman (1998) suggests the degree to which a learner is proactive or reactive to these challenges determines the degree of coping processes employed (coping strategies) and, as such, determines degree of academic success. The ELL/NSS must self-monitor, self-evaluate and finally act upon this continually repeated negotiated learning cycle to the best of their ability. So, I have adopted and adapted the five point Simplified Situated learning scenario (Box 1) to create a six point working framework for my study:

  • Box 2: Extended NSS/ELL Situated Learning Framework
    ELLs must repeatedly observe, orient, decide and act on (OODA):

    1) What do I need to know?
    2) What do I need to do?
    3) How do I do it?
    a) Analytics Interface - ongoing mediated sociocultural, sociolinguistic, socio-technical and socio-cognitive processes active in the ZPD of each learner. Learners must navigate these processes by interfacing with a continuum from explicitly to implicitly scaffolded assistance (modeled, guided, and applied) via multiple modalities and literacies:
    (1) textual (WOVE – written, oral, visual, electronic/digital)
    (2) social interactions with More Knowledgeable Other (MKO - manifest of either individuals or the internet (as a ubiquitous domain of knowledge)
    b) Navigational Road Map – ELL/NNS must make strategic choices to not just construct, but either consciously or subconsciously co-construct a schema to navigate their particular situated academic context (from objectives to outcomes)
    i) Personal Knowledge Management System (PKMS) all learners (co)construct a mediated schema for a Personal Knowledge Management System. The degree to which this is scaffolded, formal and/or informal is dependent upon the degree of:
    (1) a self-regulated awareness (comfortableness with prescribed delegation of learning responsibility – NNS have more to deal with here than NS)
    (2) a strategic learning awareness (self-efficacy – related to personal alignment of motivation, engagement and outcomes)
    4) Do it? Execute a learning plan
    a) The degree of proactive or reactive response to prescribed tasks (coping strategies)
    b) Deploy personal repertoire of learning strategies - self-evaluation and monitoring or their:
    (1) a strategic linguistic literacy awareness
    (2) a strategic communicative literacy awareness
    (a) a strategic intercultural awareness
    (3) a strategic academic literacy awareness
    (4) strategic digital literacy awareness
    5) How did I do and how can I do better? (outcomes) - completion of syllabus Scenario(s) for formative and summative assessment which feeds back and forward into the cycle. Engeström (2011) proposes that this is best addressed by Vygotsky’s proposed notion of double stimulation best captured in what he proposes as progressive forms of formative interventions (see his critique of Design Research (Ibid).
    Adapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23

  • This cycle is a continually repeated in the academic task completion process. The benefit of this approach is it captures the repeatable processes that learners employ to complete tasks and assessments throughout an academic course of study. It will attempt to query student attitudes, strategies and practices that learners employ to process the cognitive, social and cultural aspects of the various interactions interventions, and mediations between the learner and MKO. These mediations are increasing digitally mediated.
    Analytics for Institutional Processes: LMS/VLE
    A seminal case of analytics in use includes Western Governors University use of assessment-based coaching reports to develop customized study plans for students.
    • Working successfully with NNS, students having diverse educational backgrounds and abilities in self-directed learning demands diverse approaches, including the customization of study plans supported by a technology- and analytics-driven solutions.
    • Universities provide students and their faculty mentors an online coaching report based on each student’s pre-assessment and assessment results by topic area.
    • The customized study plans and the Coaching Report formative programmes resulted in measured increases of almost 4% in student performance (Levin and Johnstone, 2012).
    Likewise, the University of Queensland, in Australia utilizes a Navigational Road Map LMS strategy for its engineering programme. The programme organizes learners into 4-5 for the semester. It utilizes an integrated syllabus design highlighted by problem-based activities and four major project-based assessments for the semester.Other cases include Purdue University’s Signals initiative uses grade information, demographics, and existing student data on effort and engagement to provide students with early performance notifications that have resulted in higher grades and an increased tendency to seek out help. Paul Smith’s College used analytics to improve its early-alert programme, providing more efficient and more effective interventions that resulted in increased success, persistence, and graduation rates. The Open University of the UK, identified at-risk students through mining their existing VLE, thus improving student retention rates (Ibid: 5).
    Bottom line, these cases demonstrate that an analytics approach to my [Institutional] LMS/VLE  strategic learners/ ZPD  PLE [student] continuum can be adapted to help ELLs make better strategic and actionable choices to improve their linguistic, communicative and academic literacies [process]; but more strategically improve measured performance [product] and as such achieve greater success in their academic disciplines. It is the actionable use of this repeatable analytics process that gives this kind of research advantages over traditional of forms analysis and reporting (ibid:7). It is in effect action research.
    Analytics for Personal Learning Processes: PLEs and ELL Repertoire of Learning Strategies
    A Personal Learning Environment (PLE) is defined as “a set of conscious strategies to use technological tools to gain access to the knowledge contained in objects and people and, through that, achieve specific learning goals (Pena-Lopez, 2012: 2). If language learning is going the way of self-regulated pluralingualism (CEFR et al); then the exploration of the ZPD between formal language resources and personally accessed informal resources has major ramifications for language acquisition in an increasingly digitally mediated lifestyle and learning environment. “The PLE links what is formal with what is autodidactic and what is institutional with what is not” (Ibid: 8). The ZPD/PLE relationship is illustrated in Figure 1 below.

  • Source: Peña-López, I. (2012) “Personal Learning Environments and the revolution of Vygotsky’s Zone of Proximal Development” In ICTlogy, #107, August 2012. Barcelona: ICTlogy. Retrieved from http://ictlogy.net/review/?p=3981

  • Within the theoretical framework outlined earlier, the data collection strategy is structured around gathering data on these three fundamental learner objectives and outcomes:
    ELLs must repeatedly observe, orient, decide and act on (OODA):

    1) What do I need to know?
    2) What do I need to do?
    3) How do I do it? I need a strategy
    a) Analytics Interface - ongoing personal diagnostics feedback/feedforward based on MKO mediation and peer interactions/activities that feed into an ongoing LMS/PLE negotiated learning processes that they must self-monitor, self-evaluate and act to the best of their ability.
    b) Navigational Road Map
    4) Do it! Execute the plan (coping strategies)
    5) How did I do and how can I do better?

    Adapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23

    To this end I will endeavor to generate several sets of data:
    Core Goal
    Data Set A: Core Goal of this Study – Learning Resource Substitution Patterns
    Sub Goals
    Data Set B: Establishing ELL Digital Profiles
    Data Set C: Learning Strategies for Syllabus Task Completion Scenarios
    To achieve these core goals, more specific sub-processes of this study include:
     Collection of data on the attitudes, perceptions, and usage of various devices and modes
     Collection of data on NNS accessing which digitally mediated learning resources in their L1 and/or L2
     Attitudes on help or hindrance to productivity and positive/negative perceptions of impact on student workload
     Student evaluation on effectiveness of a) devices b) applications (Education/Pedagogy 2.0)
     Collection of data on the attitudes, perceptions and practices of NNS interaction and engagement with VLE/LMS (i.e. Moodle)
    Data Set A: Core Goal of this Study – Learning Resource Substitution Patterns
    Method: Dedicated Survey#1: Switching & Substitution Patterns
    Survey design tools:
    • Oxford university technology experience surveys 2007-2008 – can be used as a scaffold - https://wiki.brookes.ac.uk/display/JISCle2m/Thema+Surveys#ThemaSurveys-Reflectivesurvey
    • Iowa State University WOVE communication across the curriculum approach to TEAP
    • (US) National Study of Undergraduate Students and Technology 2012 and 2013 - EDUCAUSE Center for Applied Research (ECAR) http://www.educause.edu/library/resources/ecar-study-undergraduate-students-and-information-technology-2013
    • Open University UK has some academic literacy diagnostic – trying to access

    Data for this critical component of the study is based on the elements presented in Figure 4.4.2 below. It depicts collecting data of ELL/NNS switching behaviour when accessing and utilizing the continuum of formal, open and informal learning resources for specific syllabus task completion scenarios. The approach is adapted from Pene-Lopez’s (2012) depiction of learner resource as presented below.

    Data Set B: Establishing ELL Digital Profiles

    Method: Dedicated Survey #2: Digital Profiler
    Survey design tool:
    iTEST - Exeter University- online diagnostic to ascertain a baseline student Digital Profile of learner digital competencies with recommendations for future strategies – available from JISC - http://jiscdesignstudio.pbworks.com/w/page/66088990/iTest

    Data that incorporate ELL/NSS strategies to attain linguistic, communicative and academic competencies: Reading, Listening, Speaking, Writing, Research (study), Digital, Intercultural. A data strategy that captures these dynamics should allow me to generate digital profiles of the ELL. Analysis of the data should reveal diagnostic data on various digital user profiles:

     Digital dodger
     Digital guru
     Information junkie
     Career builder
     Media mogul
     Online networker (Source: itest, Exeter University)

    Data Set C: Learning Strategies for Syllabus Task Completion Scenarios

    Method: Dedicated Survey #3: Learning Strategies and Multimodal Practice
    Survey design tools:
    i) Motivated Strategies for Learning Questionnaire (MSLQ)
    (ii) Strategy Inventory for Language Learning (SILL)

    (i) Motivated Strategies for Learning Questionnaire (MSLQ)
    The Motivated Strategies for Learning Questionnaire (MSLQ), developed by Pintrich and his colleagues, is a widely used self-report instrument designed to assess college students’ motivational orientations and their use of different learning strategies.

    Sample Excerpt - Table 1: Descriptive statistics of motivated strategies for learning questionnaire (MSLQ) for Korean American students1.


    Scales Sub-scales Mean SD


    Motivation scales Intrinsic goal orientation 4.88 1.509
    Extrinsic goal orientation 4.99 1.578
    Task value 5.19 1.413
    Control of learning beliefs 5.25 1.471
    Self-efficacy for learning and performance 4.9 1.317
    Test anxiety 4.88 1.729


    Learning strategy scales Rehearsal 4.76 1.665
    Elaboration 4.67 1.619
    Organization 4.66 1.690
    Critical thinking 4.36 1.518
    Metacognitive self-regulation 4.46 1.593
    Time and study environment management 4.48 1.717
    Effort regulation 4.15 1.722
    Help seeking 4.53 1.772
    Peer learning 3.5 1.762


    Note: 1Minimum and maximum scores are based on 7-point Likert scale (1: Not at all and 7: Very true of me). Source: http://www.hindawi.com/journals/edu/2011/491276/

    (ii) Strategy Inventory for Language Learning (SILL)
    The Strategy Inventory for Language Learning (SILL) is designed to examine students’ reported frequency of use of six systems of language learning strategies. The six systems, proposed by Oxford [41], include three direct language learning strategies (cognitive, memory, and compensatory strategies) and three indirect language learning strategies (metacognitive, affective, and social strategies).
    (iii) Understanding the Self-Regulated ELL/NNS (Zimmerman)
    A number of self-regulatory processes that are important to academic studying have been identified (Zimmerman, 1998; 2002). These include
    • goal setting strategies
    • help seeking strategies – the ELL/NSS strategies and practices 1) textual 2) social interaction
    o Become member of Personal Learning Network (PLN)
    • task completion strategies
    o Productivity tools
    o Study/research strategies
    • imagery self-instruction
    • time management – organizing applications
    • self-monitoring,
    • self-evaluation – analytics interface – degree to which internalizing diagnostic tool results for improved learning
    • self-sequences,
    • environmental restructuring
    A number of studies have confirmed that these self-regulatory processes are important for academic achievement, and that high achievers engage in almost all of these processes much more frequently than low achievers (Purdie, Hattie, & Douglas, 1996).

  • Potential Problem area (SPSS): Unfortunately, I assume I will gather data from these surveys 1-3, and run it through some SPSS to generate some enhanced data for analysis. I have never used SPSS before, so we’ll see (mean, standard variation, elements of similar ilk). SPSS should yield attitude, navigational and switching behaviours. I am curious about L1 and L2 usage and to what degree for particular tasks, modes and purposes. I could not seem to find too many studies that bore this information.
    Additional points to consider:
     Zimmerman (1998) makes distinctions between naïve and skilled self-regulated learners and their distinctive behaviours. I will put elements in the surveys that should generate data to profile potential naïve and skilled learners (Not sure if this is ELL related. I will have to bear that in mind).
     Do ELLs explicitly or implicitly develop their Navigational Road Maps (transmedia navigation), if at all: How much structure and scaffolded guidance is required by ELL
     Analysis of the differences in attitudes and practices that may occur between male and female students
     Analysis of the analytics interface between ELL, their digital practice and situated institutional attempts to integrate digital practice into communication and teaching strategies.
    One should note these language development elements are formative in nature. When coupled with evidence of more summative assessments such as tests, readings, major written papers, projects and planning documents then one has a whole range of language use evidence reflecting a much broader and comprehensive range of skills; namely academic literacies.
    Ethical Issues – don’t foresee any apart from students not agreeing to participate

{"cards":[{"_id":"3730957126cb89c427000006","treeId":"371abd677cf2c9b695000017","seq":1,"position":1,"parentId":null,"content":"#Part 1: Theoretical Perspectives#\n\n\n"},{"_id":"37309b5f26cb89c427000008","treeId":"371abd677cf2c9b695000017","seq":1,"position":1,"parentId":"3730957126cb89c427000006","content":"There are key philosophical assumptions that underlay the integrated components of this research study. Engeström’s (2001, 2011) Activity theory and Park’s (2011) merging of Activity Theory with Transactional Distance Theory (Moore, 1997) lies at the heart of relating Vygotsky’s key semiotic tool (that of language) to a digitally mediated representation of his zone of proximal development (ZPD) (Engeström, 1987, 2001; Fallon, 2011; Motteram, 2013; Vygotsky, 1978, 1994). I adopt Pena-Lopez’s (2012) developmental model of the blurred boundary and interface between formal educational Learning Management Systems (LMS)/Virtual Learning Environment (VLE) and the individual learner’s Personal Learning Environment (PLE)/Personal Knowledge Management System (PKMS) (see also Atwell, 2009, 2011). Finally, I further adapt this model to the experiences of the ELL/NNS in the English mediated HE context. It is within this English mediated context that I will attempt to generate survey data that tracks ELL/NNS strategic substitution and switching patterns as they access and utilize a continuum of formal, open and informal learning resources and also address to what degree these substitution patterns happen in their L1 and/or L2. The study will further attempt to analyse the sub processes that account for the degree ELL/NNS a) further develop linguistic, communicative and academic competencies; and b) the processes by which they successfully (or unsuccessfully) complete requisite formative and summative academic tasks and assessments."},{"_id":"37309e3726cb89c42700000a","treeId":"371abd677cf2c9b695000017","seq":1,"position":2,"parentId":"3730957126cb89c427000006","content":"\n\n[formal learning] LMS/VLE  ZPD  PLE/PKMS [informal learning]\nAnalytics interface\nDigitally mediated learning resources:\nELL/NNS substitution/switching patterns (L1 + L2)\n\n\nSo, the core question is:\n\nWhat switching and substitution practices on the continuum of formal to informal multimodal learning resources do graduate ELLs/NNS use to complete the diverse types of coursework and summative tasks and assessments in a UK HE institution? \n\n\n"},{"_id":"3730a64926cb89c42700000c","treeId":"371abd677cf2c9b695000017","seq":1,"position":2,"parentId":null,"content":"#Part 2: Research Method#\n"},{"_id":"3730aee826cb89c42700000f","treeId":"371abd677cf2c9b695000017","seq":1,"position":1,"parentId":"3730a64926cb89c42700000c","content":"Lea and Jones (2010) offer a critique much of current research in mLearning, elearning and digitally enhanced learning. They cite a lack of theoretical modeling in research studies on learning and technology. These findings are similar to Viberg and Grolund (2012) meta-analysis of mLearning studies from 2007-2012. The theoretical framework presented in this study attempts to run a critical path (a red thread) throughout the full developmental cycle of the study (see theoretical framework outlined above). So what methodology will generate the data to address the core question?\nThe Use of an Analytics Approach \nEDUCAUSE (2013) posits a working definition of analytics as “the use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues.” However, practical applications suggest the addition of “prediction”, “strategic” and “decision-making”. As a model to understand ELL strategic choices in regulating their linguistic, communicative and academic development analytics serves as a useful framework of analysis. It was established earlier the emphasis of this study is process over product with respect to the socially situated ELL experience in HE. A focus group of educational practitioners describe analytics as \n1.\tstarting with a strategic question\n2.\tfinding or collecting the appropriate data to answer that question\n3.\tanalyzing the data with an eye toward prediction and insight\n4.\trepresenting or presenting findings in ways that are both understandable and actionable, and\n5.\tfeeding back into the process of addressing strategic questions and creating new ones (p.6).\nThe advent of digitally mediated learning environments has created an opportunity in education to digitally track learning transactions and automatically obtain repeatable data in HE. It is the actionable use of this repeatable analytics process that gives this kind of research advantages over traditional of forms analysis and reporting (ibid:7). It is in effect actionable research. \n"},{"_id":"3730b1a626cb89c427000010","treeId":"371abd677cf2c9b695000017","seq":1,"position":2,"parentId":"3730a64926cb89c42700000c","content":"ELLs must repeatedly observe, orient, decide and act on (OODA):\n\n1)\tWhat do I need to know?\n2)\tWhat do I need to do?\n3)\tHow do I do it? I need a strategy \na)\tAnalytics Interface - ongoing personal diagnostics feedback/feedforward based on MKO mediation and peer interactions/activities that feed into an ongoing LMS/PLE negotiated learning processes that they must self-monitor, self-evaluate and act to the best of their ability.\nb)\tNavigational Road Map\n4)\tDo it! Execute the plan (coping strategies)\n5)\tHow did I do and how can I do better? \n\nAdapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23\n\n"},{"_id":"3730b2fe26cb89c427000011","treeId":"371abd677cf2c9b695000017","seq":1,"position":3,"parentId":"3730a64926cb89c42700000c","content":"To this end I will endeavor to generate several sets of data: \nCore Goal\nData Set A: Core Goal of this Study – Learning Resource Substitution Patterns\t\nSub Goals\nData Set B: Establishing ELL Digital Profiles\t\nData Set C: Learning Strategies for Syllabus Task Completion Scenarios\t\nTo achieve these core goals, more specific sub-processes of this study include:\n\tCollection of data on the attitudes, perceptions, and usage of various devices and modes \n\tCollection of data on NNS accessing which digitally mediated learning resources in their L1 and/or L2 \n\tAttitudes on help or hindrance to productivity and positive/negative perceptions of impact on student workload\n\tStudent evaluation on effectiveness of a) devices b) applications (Education/Pedagogy 2.0)\n\tCollection of data on the attitudes, perceptions and practices of NNS interaction and engagement with VLE/LMS (i.e. Moodle)\n"},{"_id":"3730b45526cb89c427000012","treeId":"371abd677cf2c9b695000017","seq":1,"position":4,"parentId":"3730a64926cb89c42700000c","content":"\n[formal learning] LMS/VLE  ZPD  PLE/PKMS [informal learning]\nAnalytics interface\nDigitally mediated learning resources:\nELL/NNS substitution/switching patterns (L1 + L2)\n\n\nSo, the core question is:\n\nWhat switching and substitution practices on the continuum of formal to informal multimodal learning resources do graduate ELLs/NNS use to complete the diverse types of coursework and summative tasks and assessments in a UK HE institution? \n\n"},{"_id":"3730b6a826cb89c427000013","treeId":"371abd677cf2c9b695000017","seq":1,"position":5,"parentId":"3730a64926cb89c42700000c","content":"Such switching strategies will give empirical evidence as to what happens in the digitally mediated interface between Personal Learning Environments (PLE) and Formal Educational Learning Management Systems (LMS)/VLEs.\n\nTo this end, I will endeavor to generate several data sets: \nCore Goal\nData Set A: Core Goal of this Study – Learning Resource Substitution Patterns\t\nSub Goals\nData Set B: Determining Student (ELL) Digital Profiles\t\nData Set C: Learning Strategies for Syllabus Task Completion Scenarios\t\nThe Primacy of the Analytics Interface for the ELL\nOne of the major goals of this study is to better understand how students employ strategies to navigate a) prescribed road maps (scaffolded) b) personally constructed road maps employed to access the necessary learning resources to achieve academic success. The navigational road map focuses learner objectives and outcomes mediated through their own PLE. What demands further research is how the NNS/ELL plans for and navigates English and digitally mediated academic environments through strategic choices that are both socially and culturally situated. These strategic choices are further formally or informally structured around five fundamental learner objectives and outcomes present in any given situated learning scenario:\n"},{"_id":"3730b76a26cb89c427000014","treeId":"371abd677cf2c9b695000017","seq":1,"position":6,"parentId":"3730a64926cb89c42700000c","content":"Box 1: The Simplified ELL/NSS Situated Learning Framework\n\nELLs must repeatedly observe, orient, decide and act on (OODA):\n\n1.\tWhat do I need to know?\n2.\tWhat do I need to do?\n3.\tHow do I do it? I need a schema\n3.1.\tAnalytics Interface \n3.2.\tNavigational Road Map\n4.\tDo it! Execute the plan (coping strategies)\n5.\tHow did I do and how can I do better? \n\nAdapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23\n\n"},{"_id":"3730b93d26cb89c427000015","treeId":"371abd677cf2c9b695000017","seq":1,"position":7,"parentId":"3730a64926cb89c42700000c","content":"These five rather general objectives and outcomes, and in rather pragmatic terms, are representative of the ELL experience in a HE learning environment. The analytics interface is that blurred boundary where the institutions learning management interface, the MKO (the instructors, mentors, tutors) and the personal learning environment of ELL all coalesce to construct new learner knowledge and enhanced academic literacy. The mediations that feedback and forward analytical data to the ELL/NSS must process are complex and unless appropriately modeled, scaffolded and applied, can by quite convoluted, confusing and disorienting. Zimmerman (1998) suggests the degree to which a learner is proactive or reactive to these challenges determines the degree of coping processes employed (coping strategies) and, as such, determines degree of academic success. The ELL/NSS must self-monitor, self-evaluate and finally act upon this continually repeated negotiated learning cycle to the best of their ability. So, I have adopted and adapted the five point Simplified Situated learning scenario (Box 1) to create a six point working framework for my study:"},{"_id":"3730ba0226cb89c427000016","treeId":"371abd677cf2c9b695000017","seq":1,"position":8,"parentId":"3730a64926cb89c42700000c","content":"Box 2: Extended NSS/ELL Situated Learning Framework\nELLs must repeatedly observe, orient, decide and act on (OODA):\n\n1)\tWhat do I need to know?\n2)\tWhat do I need to do?\n3)\tHow do I do it?\na)\tAnalytics Interface - ongoing mediated sociocultural, sociolinguistic, socio-technical and socio-cognitive processes active in the ZPD of each learner. Learners must navigate these processes by interfacing with a continuum from explicitly to implicitly scaffolded assistance (modeled, guided, and applied) via multiple modalities and literacies:\n(1)\ttextual (WOVE – written, oral, visual, electronic/digital)\n(2)\tsocial interactions with More Knowledgeable Other (MKO - manifest of either individuals or the internet (as a ubiquitous domain of knowledge)\nb)\tNavigational Road Map – ELL/NNS must make strategic choices to not just construct, but either consciously or subconsciously co-construct a schema to navigate their particular situated academic context (from objectives to outcomes)\ni)\tPersonal Knowledge Management System (PKMS) all learners (co)construct a mediated schema for a Personal Knowledge Management System. The degree to which this is scaffolded, formal and/or informal is dependent upon the degree of:\n(1)\ta self-regulated awareness (comfortableness with prescribed delegation of learning responsibility – NNS have more to deal with here than NS)\n(2)\ta strategic learning awareness (self-efficacy – related to personal alignment of motivation, engagement and outcomes)\n4)\tDo it? Execute a learning plan\na)\tThe degree of proactive or reactive response to prescribed tasks (coping strategies)\nb)\tDeploy personal repertoire of learning strategies - self-evaluation and monitoring or their: \n(1)\ta strategic linguistic literacy awareness\n(2)\ta strategic communicative literacy awareness\n(a)\ta strategic intercultural awareness\n(3)\ta strategic academic literacy awareness\n(4)\tstrategic digital literacy awareness\n5)\tHow did I do and how can I do better? (outcomes) - completion of syllabus Scenario(s) for formative and summative assessment which feeds back and forward into the cycle. Engeström (2011) proposes that this is best addressed by Vygotsky’s proposed notion of double stimulation best captured in what he proposes as progressive forms of formative interventions (see his critique of Design Research (Ibid).\nAdapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23\n\n"},{"_id":"3730bb4e26cb89c427000017","treeId":"371abd677cf2c9b695000017","seq":1,"position":9,"parentId":"3730a64926cb89c42700000c","content":"This cycle is a continually repeated in the academic task completion process. The benefit of this approach is it captures the repeatable processes that learners employ to complete tasks and assessments throughout an academic course of study. It will attempt to query student attitudes, strategies and practices that learners employ to process the cognitive, social and cultural aspects of the various interactions interventions, and mediations between the learner and MKO. These mediations are increasing digitally mediated. \nAnalytics for Institutional Processes: LMS/VLE\nA seminal case of analytics in use includes Western Governors University use of assessment-based coaching reports to develop customized study plans for students. \n•\tWorking successfully with NNS, students having diverse educational backgrounds and abilities in self-directed learning demands diverse approaches, including the customization of study plans supported by a technology- and analytics-driven solutions.\n•\tUniversities provide students and their faculty mentors an online coaching report based on each student's pre-assessment and assessment results by topic area.\n•\tThe customized study plans and the Coaching Report formative programmes resulted in measured increases of almost 4% in student performance (Levin and Johnstone, 2012).\nLikewise, the University of Queensland, in Australia utilizes a Navigational Road Map LMS strategy for its engineering programme. The programme organizes learners into 4-5 for the semester. It utilizes an integrated syllabus design highlighted by problem-based activities and four major project-based assessments for the semester.Other cases include Purdue University’s Signals initiative uses grade information, demographics, and existing student data on effort and engagement to provide students with early performance notifications that have resulted in higher grades and an increased tendency to seek out help. Paul Smith’s College used analytics to improve its early-alert programme, providing more efficient and more effective interventions that resulted in increased success, persistence, and graduation rates. The Open University of the UK, identified at-risk students through mining their existing VLE, thus improving student retention rates (Ibid: 5). \nBottom line, these cases demonstrate that an analytics approach to my [Institutional] LMS/VLE  strategic learners/ ZPD  PLE [student] continuum can be adapted to help ELLs make better strategic and actionable choices to improve their linguistic, communicative and academic literacies [process]; but more strategically improve measured performance [product] and as such achieve greater success in their academic disciplines. It is the actionable use of this repeatable analytics process that gives this kind of research advantages over traditional of forms analysis and reporting (ibid:7). It is in effect action research. \nAnalytics for Personal Learning Processes: PLEs and ELL Repertoire of Learning Strategies\nA Personal Learning Environment (PLE) is defined as “a set of conscious strategies to use technological tools to gain access to the knowledge contained in objects and people and, through that, achieve specific learning goals (Pena-Lopez, 2012: 2). If language learning is going the way of self-regulated pluralingualism (CEFR et al); then the exploration of the ZPD between formal language resources and personally accessed informal resources has major ramifications for language acquisition in an increasingly digitally mediated lifestyle and learning environment. “The PLE links what is formal with what is autodidactic and what is institutional with what is not” (Ibid: 8). The ZPD/PLE relationship is illustrated in Figure 1 below.\n"},{"_id":"3730bceb26cb89c427000018","treeId":"371abd677cf2c9b695000017","seq":1,"position":10,"parentId":"3730a64926cb89c42700000c","content":" \t \nSource: Peña-López, I. (2012) “Personal Learning Environments and the revolution of Vygotsky’s Zone of Proximal Development” In ICTlogy, #107, August 2012. Barcelona: ICTlogy. Retrieved from http://ictlogy.net/review/?p=3981\n\n"},{"_id":"3730aba226cb89c42700000d","treeId":"371abd677cf2c9b695000017","seq":1,"position":3,"parentId":null,"content":"#Part 3: Data Collection Techniques#\n"},{"_id":"3730c97b26cb89c427000019","treeId":"371abd677cf2c9b695000017","seq":1,"position":1,"parentId":"3730aba226cb89c42700000d","content":"Within the theoretical framework outlined earlier, the data collection strategy is structured around gathering data on these three fundamental learner objectives and outcomes:\nELLs must repeatedly observe, orient, decide and act on (OODA):\n\n1)\tWhat do I need to know?\n2)\tWhat do I need to do?\n3)\tHow do I do it? I need a strategy \na)\tAnalytics Interface - ongoing personal diagnostics feedback/feedforward based on MKO mediation and peer interactions/activities that feed into an ongoing LMS/PLE negotiated learning processes that they must self-monitor, self-evaluate and act to the best of their ability.\nb)\tNavigational Road Map\n4)\tDo it! Execute the plan (coping strategies)\n5)\tHow did I do and how can I do better? \n\nAdapted from : http://www.slideshare.net/fullscreen/ELESIGpresentations/elesig-carl-lydia28june2013final/23\n\n\n\n\n\nTo this end I will endeavor to generate several sets of data: \nCore Goal\nData Set A: Core Goal of this Study – Learning Resource Substitution Patterns\t\nSub Goals\nData Set B: Establishing ELL Digital Profiles\t\nData Set C: Learning Strategies for Syllabus Task Completion Scenarios\t\nTo achieve these core goals, more specific sub-processes of this study include:\n\tCollection of data on the attitudes, perceptions, and usage of various devices and modes \n\tCollection of data on NNS accessing which digitally mediated learning resources in their L1 and/or L2 \n\tAttitudes on help or hindrance to productivity and positive/negative perceptions of impact on student workload\n\tStudent evaluation on effectiveness of a) devices b) applications (Education/Pedagogy 2.0)\n\tCollection of data on the attitudes, perceptions and practices of NNS interaction and engagement with VLE/LMS (i.e. Moodle)\nData Set A: Core Goal of this Study – Learning Resource Substitution Patterns\nMethod: Dedicated Survey#1: Switching & Substitution Patterns\nSurvey design tools:\n•\t Oxford university technology experience surveys 2007-2008 – can be used as a scaffold - https://wiki.brookes.ac.uk/display/JISCle2m/Thema+Surveys#ThemaSurveys-Reflectivesurvey\n•\tIowa State University WOVE communication across the curriculum approach to TEAP\n•\t (US) National Study of Undergraduate Students and Technology 2012 and 2013 - EDUCAUSE Center for Applied Research (ECAR) http://www.educause.edu/library/resources/ecar-study-undergraduate-students-and-information-technology-2013\n•\tOpen University UK has some academic literacy diagnostic – trying to access\n\nData for this critical component of the study is based on the elements presented in Figure 4.4.2 below. It depicts collecting data of ELL/NNS switching behaviour when accessing and utilizing the continuum of formal, open and informal learning resources for specific syllabus task completion scenarios. The approach is adapted from Pene-Lopez’s (2012) depiction of learner resource as presented below.\n \n\n\nData Set B: Establishing ELL Digital Profiles \n\nMethod: Dedicated Survey #2: Digital Profiler\nSurvey design tool:\niTEST - Exeter University- online diagnostic to ascertain a baseline student Digital Profile of learner digital competencies with recommendations for future strategies – available from JISC - http://jiscdesignstudio.pbworks.com/w/page/66088990/iTest\n\n\nData that incorporate ELL/NSS strategies to attain linguistic, communicative and academic competencies: Reading, Listening, Speaking, Writing, Research (study), Digital, Intercultural. A data strategy that captures these dynamics should allow me to generate digital profiles of the ELL. Analysis of the data should reveal diagnostic data on various digital user profiles: \n\n\tDigital dodger\n\tDigital guru\n\tInformation junkie\n\tCareer builder\n\tMedia mogul\n\tOnline networker (Source: itest, Exeter University)\n\nData Set C: Learning Strategies for Syllabus Task Completion Scenarios \n\nMethod: Dedicated Survey #3: Learning Strategies and Multimodal Practice\nSurvey design tools:\ni) Motivated Strategies for Learning Questionnaire (MSLQ)\n(ii) Strategy Inventory for Language Learning (SILL)\n\n\n(i)\tMotivated Strategies for Learning Questionnaire (MSLQ)\nThe Motivated Strategies for Learning Questionnaire (MSLQ), developed by Pintrich and his colleagues, is a widely used self-report instrument designed to assess college students' motivational orientations and their use of different learning strategies.\n\nSample Excerpt - Table 1: Descriptive statistics of motivated strategies for learning questionnaire (MSLQ) for Korean American students1.\n________________________________________\nScales\tSub-scales\tMean\tSD\n________________________________________\n\nMotivation scales\tIntrinsic goal orientation\t4.88\t1.509\n\tExtrinsic goal orientation\t4.99\t1.578\n\tTask value\t5.19\t1.413\n\tControl of learning beliefs\t5.25\t1.471\n\tSelf-efficacy for learning and performance\t4.9\t1.317\n\tTest anxiety\t4.88\t1.729\n________________________________________\nLearning strategy scales\tRehearsal\t4.76\t1.665\n\tElaboration\t4.67\t1.619\n\tOrganization\t4.66\t1.690\n\tCritical thinking\t4.36\t1.518\n\tMetacognitive self-regulation\t4.46\t1.593\n\tTime and study environment management\t4.48\t1.717\n\tEffort regulation\t4.15\t1.722\n\tHelp seeking\t4.53\t1.772\n\tPeer learning\t3.5\t1.762\n________________________________________\n\nNote: 1Minimum and maximum scores are based on 7-point Likert scale (1: Not at all and 7: Very true of me). Source: http://www.hindawi.com/journals/edu/2011/491276/\n\n(ii) Strategy Inventory for Language Learning (SILL)\nThe Strategy Inventory for Language Learning (SILL) is designed to examine students' reported frequency of use of six systems of language learning strategies. The six systems, proposed by Oxford [41], include three direct language learning strategies (cognitive, memory, and compensatory strategies) and three indirect language learning strategies (metacognitive, affective, and social strategies).\n(iii) Understanding the Self-Regulated ELL/NNS (Zimmerman)\nA number of self-regulatory processes that are important to academic studying have been identified (Zimmerman, 1998; 2002). These include \n•\tgoal setting strategies\n•\thelp seeking strategies – the ELL/NSS strategies and practices 1) textual 2) social interaction\no\tBecome member of Personal Learning Network (PLN)\n•\t task completion strategies \no\tProductivity tools \no\tStudy/research strategies\n•\timagery self-instruction\n•\ttime management – organizing applications\n•\tself-monitoring, \n•\tself-evaluation – analytics interface – degree to which internalizing diagnostic tool results for improved learning\n•\tself-sequences, \n•\tenvironmental restructuring\nA number of studies have confirmed that these self-regulatory processes are important for academic achievement, and that high achievers engage in almost all of these processes much more frequently than low achievers (Purdie, Hattie, & Douglas, 1996). \n"},{"_id":"3730cae226cb89c42700001a","treeId":"371abd677cf2c9b695000017","seq":487070,"position":2,"parentId":"3730aba226cb89c42700000d","content":"Potential Problem area (SPSS): Unfortunately, I assume I will gather data from these surveys 1-3, and run it through some SPSS to generate some enhanced data for analysis. I have never used SPSS before, so we’ll see (mean, standard variation, elements of similar ilk). SPSS should yield attitude, navigational and switching behaviours. I am curious about L1 and L2 usage and to what degree for particular tasks, modes and purposes. I could not seem to find too many studies that bore this information.\nAdditional points to consider:\n\tZimmerman (1998) makes distinctions between naïve and skilled self-regulated learners and their distinctive behaviours. I will put elements in the surveys that should generate data to profile potential naïve and skilled learners (Not sure if this is ELL related. I will have to bear that in mind).\n\tDo ELLs explicitly or implicitly develop their Navigational Road Maps (transmedia navigation), if at all: How much structure and scaffolded guidance is required by ELL \n\tAnalysis of the differences in attitudes and practices that may occur between male and female students\n\tAnalysis of the analytics interface between ELL, their digital practice and situated institutional attempts to integrate digital practice into communication and teaching strategies.\nOne should note these language development elements are formative in nature. When coupled with evidence of more summative assessments such as tests, readings, major written papers, projects and planning documents then one has a whole range of language use evidence reflecting a much broader and comprehensive range of skills; namely academic literacies. \nEthical Issues – don’t foresee any apart from students not agreeing to participate\n"},{"_id":"3730ad5426cb89c42700000e","treeId":"371abd677cf2c9b695000017","seq":1,"position":4,"parentId":null,"content":"#Part 4: Data Analysis Approach#\n"}],"tree":{"_id":"371abd677cf2c9b695000017","name":"Dissertation Proposal Oct 5","publicUrl":"dissertation-proposal-oct-5"}}