Cognitive load theory is built upon two commonly accepted ideas. The first is that there is a limit to how much new information the human brain can process at one time. The second is that there are no known limits to how much stored information can be processed at one time. The aim of cognitive load research is therefore to develop instructional techniques and recommendations that fit within the characteristics of working memory, in order to maximise learning.
Explicit instruction involves teachers clearly showing students what to do and how to do it, rather than having students discover or construct information for themselves
how working memory and long-term memory process and store information
Working memory is the memory system where small amounts of information are stored for a very short duration (RAM). Long-term memory is the memory system where large amounts of information are stored semi-permanently (hard drive)
Cognitive load theory assumes that knowledge is stored in long- term memory in the form of ‘schemas’ 2 . A schema organises elements of information according to how they will be used. According to schema theory, skilled performance is developed through building ever greater numbers of increasingly complex schemas by combining elements of lower level schemas into higher level schemas. There is no limit to how complex schemas can become. An important process in schema construction is automation, whereby information can be processed automatically with minimal conscious effort. Automaticity occurs after extensive practice
Schemas provide a number of important functions that are relevant to learning. First, they provide a system for organising and storing knowledge. Second, and crucially for cognitive load theory, they reduce working memory load. This is because, although there are a limited number of elements that can be held in working memory at one time, a schema constitutes only a single element in working memory. In this way, a high level schema – with potentially infinite informational complexity – can effectively bypass the limits of working memory
Types of cognitive load
Cognitive load theory identifies three different types of cognitive load: intrinsic, extraneous and germane load
Intrinsic cognitive load relates to the inherent difficulty of the subject matter being learnt.
subject matter that is difficult for a novice may be very easy for an expert.
Extraneous cognitive load relates to how the subject matter is taught.
extraneous load is the ‘bad’ type of cognitive load, because it does not directly contribute to learning. Cognitive load theorists consider that instructional design will be most effective when it minimises extraneous load in order to free up the capacity of working memory
Germane cognitive load refers to the load imposed on the working memory by the process of learning – that is, the process of transferring information into the long-term memory through schema construction
the approach of decreasing extraneous cognitive load while increasing germane cognitive load will only be effective if the total cognitive load remains within the limits of working memory
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Learning to Harness Big Data in an Academic Library
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
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Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
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Flipped classroom is an instructional strategy and a type of blended learning that reverses the traditional educational arrangement by delivering instructional content, often online, outside of the classroom.
In essence, “flipping the classroom” means that students gain first exposure to new material outside of class, usually via reading or lecture videos, and then use class time to do the harder work of assimilating that knowledge, perhaps through problem-solving, discussion, or debates.
although learning styletheories serve as a justification for different learning activities it does not provide the necessarytheoretical framework as to how the activities need to be structured (Bishop and Verleger, 2013). p. 99
One observation from the literature is there is a lack of consistency of models of the FCM (Davieset al.,2013, p. 565) in addition to a lack of research into student performance, (Findlay-Thompson andMombourquette, 2014, p. 65; Euniceet al., 2013) broader impacts on taking up too much of thestudents’time and studies of broader student demographics. In another literature review of the FCM,Bishop and Verleger concur with the observation that there is a lack of consensus as to the definitionof the method and the theoretical frameworks (Bishop and Verleger, 2013). p. 99
The FCM isheavily reliant on technology and this is an important consideration for all who consideremploying the FCM. p. 101
Gross, B., Marinari, M., Hoffman, M., DeSimone, K., & Burke, P. (2015). Flipped @ SBU: Student Satisfaction and the College Classroom. Educational Research Quarterly, 39(2), 36-52.
we found that high levels of student engagement and course satisfaction characterised the students in the flipped courses, without any observable reduction in academic performance.
Hotle, S. L., & Garrow, L. A. (2016). Effects of the Traditional and Flipped Classrooms on Undergraduate Student Opinions and Success. Journal Of Professional Issues In Engineering Education & Practice, 142(1), 1-11. doi:10.1061/(ASCE)EI.1943-5541.0000259
It was found that student performance on quizzes was not significantly different across the traditional and flipped classrooms. A key shortcoming noted with the flipped classroom was students’ inability to ask questions during lectures. Students in flipped classrooms were more likely to attend office hours compared to traditional classroom students, but the difference was not statistically significant.
Heyborne, W. H., & Perrett, J. J. (2016). To Flip or Not to Flip? Analysis of a Flipped Classroom Pedagogy in a General Biology Course. Journal Of College Science Teaching, 45(4), 31-37.
Although the outcomes were mixed, regarding the superiority of either pedagogical approach, there does seem to be a trend toward performance gains using the flipped pedagogy. We strongly advocate for a larger multiclass study to further clarify this important pedagogical question.
Tomory, A., & Watson, S. (2015). Flipped Classrooms for Advanced Science Courses. Journal Of Science Education & Technology, 24(6), 875-887. doi:10.1007/s10956-015-9570-8
To what extent have the new generation of psychodynamic psychoanalysts addressed the issues raised by the ferocious critiques of Freud’s work that have emerged?
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Crompton, Muilenburg and Berge’s definition for m-learning is “learning across multiple contexts, through social and content interactions, using personal electronic devices.”
The “context”in this definition encompasses m-learnng that is formalself-directed, and spontaneous learning, as well as learning that is context aware and context neutral.
therefore, m-learning can occur inside or outside the classroom, participating in a formal lesson on a mobile device; it can be self-directed, as a person determines his or her own approach to satisfy a learning goal; or spontaneous learning, as a person can use the devices to look up something that has just prompted an interest (Crompton, 2013, p. 83). (Gaming article Tallinn)Constructivist Learnings in the 1980s – Following Piage’s (1929), Brunner’s (1996) and Jonassen’s (1999) educational philosophies, constructivists proffer that knowledge acquisition develops through interactions with the environment. (p. 85). The computer was no longer a conduit for the presentation of information: it was a tool for the active manipulation of that information” (Naismith, Lonsdale, Vavoula, & Sharples, 2004, p. 12)Constructionist Learning in the 1980s – Constructionism differed from constructivism as Papert (1980) posited an additional component to constructivism: students learned best when they were actively involved in constructing social objects. The tutee position. Teaching the computer to perform tasks.Problem-Based learning in the 1990s – In the PBL, students often worked in small groups of five or six to pool knowledge and resources to solve problems. Launched the sociocultural revolution, focusing on learning in out of school contexts and the acquisition of knowledge through social interaction
Socio-Constructivist Learning in the 1990s. SCL believe that social and individual processes are independent in the co-construction of knowledge (Sullivan-Palinscar, 1998; Vygotsky, 1978).
96-97). Keegan (2002) believed that e-learning was distance learning, which has been converted to e-learning through the use of technologies such as the WWW. Which electronic media and tools constituted e-learning: e.g., did it matter if the learning took place through a networked technology, or was it simply learning with an electronic device?
99-100. Traxler (2011) described five ways in which m-learning offers new learning opportunities: 1. Contingent learning, allowing learners to respond and react to the environment and changing experiences; 2. Situated learning, in which learning takes place in the surroundings applicable to the learning; 3. Authentic learning;
Diel, W. (2013). M-Learning as a subfield of open and distance education. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
15) Historical context in relation to the field of distance education (embedded librarian)
16 definition of independent study (workshop on mlearning and distance education
17. Theory of transactional distance (Moore)
Cochrane, T. (2013). A Summary and Critique of M-Learning Research and Practice. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
( Galin class, workshop)
According to Cook and Sharples (2010) the development of M learning research has been characterized by three general faces a focus upon Devices Focus on learning outside the classroom He focus on the mobility of the learner
Baby I am learning studies focus upon content delivery for small screen devices and the PDA capabilities of mobile devices rather than leveraging the potential of mobile devices for collaborative learning as recommended by hope Joyner Mill Road and sharp P. 26 Large scale am learning project Several larger am learning projects have tended to focus on specific groups of learners rather than developing pedagogical strategies for the integration of am mlearning with him tertiary education in general
m learning research funding
In comparison am learning research projects in countries with smaller population sizes such as Australia and New Zealand are typiclly funded on a shoe string budget
M-learning research methodologies
I am learning research has been predominantly characterized by short term case studies focused upon The implementation of rapidly changing technologies with early adopters but with little evaluation reflection or emphasis on mainstream tertiary-education integration
p. 29 identifying the gaps in M learning research
lack of explicit underlying pedagogical theory Lack of transferable design frameworks
Pachler, N., Bachmair, B., and Cook, J. (2013). A Sociocultural Ecological Frame for Mobile Learning. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
(Tom video studio)
35 a line of argumentation that defines mobile devices such as mobile phones as cultural resources. Mobile cultural resources emerge within what we call a “bile complex‘, which consist of specifics structures, agency and cultural practices.
36 pedagogy looks for learning in the context of identify formation of learners within a wider societal context However at the beginning of the twentieth first century and economy oriented service function of learning driven by targets and international comparisons has started to occupy education systems and schools within them Dunning 2000 describes the lengthy transformation process from natural assets Land unskilled labor to tangible assets machinery to intangible created assets such as knowledge and information of all kinds Araya and Peters 2010 describe the development of the last 20 years in terms of faces from the post industrial economy to d information economy to the digital economy to the knowledge economy to the creative economy Cultural ecology can refer to the debate about natural resources we argue for a critical debate about the new cultural resources namely mobile devices and the services for us the focus must not be on the exploitation of mobile devices and services for learning but instead on the assimilation of learning with mobiles in informal contacts of everyday life into formal education
Ecology comes into being is there exists a reciprocity between perceiver and environment translated to M learning processes this means that there is a reciprocity between the mobile devices in the activity context of everyday life and the formal learning
Rather than focusing on the acquisition of knowledge in relation to externally defined notions of relevance increasingly in a market-oriented system individual faces the challenge of shape his/her knowledge out of his/her own sense of his/her world information is material which is selected by individuals to be transformed by them into knowledge to solve a problem in the life world
Crompton, H. (2013). A Sociocultural Ecological Frame for Mobile Learning. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
p. 47 As philosophies and practice move toward learner-centered pedagogies, technology in a parallel move, is now able to provide new affordances to the learner, such as learning that is personalized, contextualized, and unrestricted by temporal and spatial constrains.
The necessity for m-learning to have a theory of its own, describing exactly what makes m-learning unique from conventional, tethered electronic learning and traditional learning.
48 . Definition and devices. Four central constructs. Learning pedagogies, technological devices, context and social interactions.
“learning across multiple contexts, through social and content interactions, using personal electronic devices.”
It is difficult, and ill advisable, to determine specifically which devices should be included in a definition of m-learning, as technologies are constantly being invented or redesigned. (my note against the notion that since D2L is a MnSCU mandated tool, it must be the one and only). One should consider m-learning as the utilization of electronic devices that are easily transported and used anytime and anywhere.
49 e-learning does not have to be networked learning: therefore, e-learnng activities could be used in the classroom setting, as the often are.
Why m-learning needs a different theory beyond e-learning. Conventional e-learning is tethered, in that students are anchored to one place while learning. What sets m-learning apart from conventional e-learning is the very lack of those special and temporal constrains; learning has portability, ubiquitous access and social connectivity.
50 dominant terms for m-learning should include spontaneous, intimate, situated, connected, informal, and personal, whereas conventional e-learning should include the terms computer, multimedia, interactive, hyperlinked, and media-rich environment.
51 Criteria for M-Learning
second consideration is that one must be cognizant of the substantial amount of learning taking place beyond the academic and workplace setting.
52 proposed theories
Activity theory: Vygotsky and Engestroem
Conversation theory: Pask 1975, cybernetic and dialectic framework for how knowledge is constructed. Laurillard (2007) although conversation is common for all forms of learning, m-learning can build in more opportunities for students to have ownership and control over what they are learning through digitally facilitated, location-specific activities.
53 multiple theories;
54 Context is central construct of mobile learning. Traxler (2011) described the role of context in m-learning as “context in the wider context”, as the notion of context becomes progressively richer. This theme fits with Nasimith et al situated theory, which describes the m-learning activities promoting authentic context and culture.
unlike e-learning, the learner is not anchored to a set place. it links to Vygotsky’s sociocultural approach.
Learning happens within various social groups and locations, providing a diverse range of connected learning experiences. furthermore, connectivity is without temporal restraints, such as the schedules of educators.
m-larning as “learning dispersed in time”
my note student-centered learning
Moura, A., Carvalho, A. (2013). Framework For Mobile Learning Integration Into Educational Contexts. In: Berge and Muilenburg (Eds.). Handbook of Mobile Learning.
students’ multimedia assignments, which lead to online resources
collaboration with other departments for the students projects
moving the class to online environment (even if kept hybrid)
What is it?
the complexity of the learning environment is turning instructional design into a more dynamic activity, responding to changing educational models and expectations. Flipped classrooms, makerspaces, and competency-based learning are changing how instructors work with students, how students work with course content, and how mastery is verified. Mobile computing, cloud computing, and data-rich repositories have altered ideas about where and how learning takes place.
How does it work?
One consequence of these changes is that designers can find themselves filling a variety of roles. Today’s instructional designer might work with subject-matter experts, coders, graphic designers, and others. Moreover, the work of an instructional designer increasingly continues throughout the duration of a course rather than taking place upfront.
Who’s doing it?
The responsibility for designing instruction traditionally fell to the instructor of a course, and in many cases it continues to do so. Given the expanding role and landscape of technology—as well as the growing body of knowledge about learning and about educational activities and assessments— dedicated instructional designers are increasingly common and often take a stronger role.
Why is it significant?
The focus on student-centered learning, for example, has spurred the creation of complex integrated learning environments that comprise multiple instructional modules. Competency-based learning allows students to progress at their own pace and finish assignments, courses, and degree plans as time and skills permit. Data provided by analytics systems can help instructional designers predict which pedagogical approaches might be most effective and tailor learning experiences accordingly. The use of mobile learning continues to grow, enabling new kinds of learning experiences.
What are the downsides?
Given the range of competencies needed for the position, finding and hiring instructional designers who fit well into particular institutional cultures can be challenging to the extent that instructors hand over greater amounts of the design process to instructional designers, some of those instructors will feel that they are giving up control, which, in some cases, might appear to be simply the latest threat to faculty authority and autonomy. My note: and this is why SCSU Academic Technology is lead by faculty not IT staff.
Where is it going?
In some contexts, instructional designers might work more directly with students, teaching them lifelong learning skills. Students might begin coursework by choosing from a menu of options, creating their own path through content, making choices about learning options, being more hands-on, and selecting best approaches for demonstrating mastery. Educational models that feature adaptive and personalized learning will increasingly be a focus of instructional design. My note: SCSU CETL does not understand instructional design tendencies AT ALL. Instead of grooming faculty to assume the the leadership role and fill out the demand for instructional design, it isolates and downgrades (keeping traditional and old-fashioned) instructional design to basic tasks of technicalities done by IT staff.
What are the implications for teaching and learning?
By helping align educational activities with a growing understanding of the conditions,
tools, and techniques that enable better learning, instructional designers can help higher education take full advantage of new and emerging models of education. Instructional
designers bring a cross-disciplinary approach to their work, showing faculty how learning activities used in particular subject areas might be effective in others. In this way, instructional
designers can cultivate a measure of consistency across courses and disciplines in how educational strategies and techniques are incorporated. Designers can also facilitate the
creation of inclusive learning environments that offer choices to students with varying strengths and preferences.
“I’d really rather work alone. . .” Most of us have heard that from a student (or several students) when we assign a group project, particularly one that’s worth a decent amount of the course grade. It doesn’t matter that the project is large,…