A Study Of The Predictive Effect Of Pre-Service Teacher Personal
Knowledge Management Competency
On Their
Instructional Design Skills
Eric Cheng, The
Hong Kong Institute of Education, Tai Po, Hong Kong
ABSTRACT:
This paper aims to examine the relationship between
the personal knowledge management (PKM) competency of pre-service teachers and
their instructional design skills. Supporting the sustainable development of teachers as professionals in
the knowledge society is a critical issue in teacher education. This study
attempts to identify an empirical model and a curriculum framework for
nurturing pre-service teachers’ PKM competency. Dorsey (2000) PKM skills were adopted for constructing the
theoretical framework and the survey instrument. A quasi-experimental
research design was used to collect data from pre-service teachers from
Keywords: Personal
knowledge management, Pre-service teacher, Teacher education
Introduction
Rapid advances in technology and communications have greatly accelerated
the emergence of information. The increases in the amounts and formats of
information available do not automatically make learners more informed or
knowledgeable, if a learner cannot manage and meld the accumulation of
information through their daily experience and study to construct knowledge in
a systematic fashion. This competency is referred by most literatures (Frand & Hixon, 1999; Dorsey, 2000; Wright, 2005) as
personal knowledge management (PKM) competency. Developing learners with PKM
competency is not simply a lifelong education issue, it is also an important
teacher education issue in terms of sustaining a competitive human capital in
the knowledge economy. Teacher development is viewed as an ongoing lifelong
learning process as teachers strive to learn how to teach learner to learn how
to learn (Cochran-Smith & Lytle, 1999).
The recent
education reforms in
Literature Review
A review of the
literature related to knowledge management suggests that the development of personal knowledge
management (PKM) could be a means of enhancing pre-service teacher professional competency in managing personal
knowledge for coping with the acceleration
of emerging information.
Frand & Hixon
(1999) define PKM as a conceptual framework to organize and integrate important
information such that it becomes part of an individual’s personal knowledge
base. Dorsey (2000) emphasizes
the importance of injecting PKM into an educational
framework for undergraduate education in order to bridge the gap between
general education and other subject disciplines. PKM could serve as a framework for
integrating general education and majors and as an approach to technology
integration initiatives throughout the curriculum. PKM provides learners with both a common language and a common
understanding of the intellectual and practical processes necessary for the
acquisition of information and its subsequent transformation into knowledge. The significance of exploring PKM may
contribute to human cognitive capabilities (Sheridan, 2008).
Scholars tend to conceptualize PKM as a set of
information skills (Frand & Hixon, 1999;
Avery et al, 2001), though there is no standard definition or model for
PKM. After Frand & Hixon (1999) outlined five PKM techniques as searching,
classifying, storing distributing, evaluating and integrating skills, Dorsey
and colleagues (Avery et al,
2001) broadened the Frand
& Hixon PKM framework well beyond its formulation. Central to PKM, as
clarified by Dorsey, are seven information skills which when exercised together
are integral to effective knowledge work. These seven PKM skills are retrieving, evaluating, organizing,
analyzing, presenting and securing information and collaboration for creating
knowledge. Recently, Pettenati and Cigognini (2009)
grouped PKM skills under three intertwined macro-competence categories:
creation, organization and sharing.
PKM can also be conceptualized as an intertwined macro-competency. Wright (2005) proposes a PKM model that links distinctive types of problem-solving activities with specific cognitive and metacognitive, information, social and learning competencies. As a learning competency, PKM enables learners to apply a set of learning skills that are essential to lifelong learning for information processing, knowledge application and decision-making. As a cognitive and metacognitve competency, it enables learners to apply complex thinking skills to solve problems. It is knowledge concerning the learner’s own cognitive processes or anything related to them (Flavell, 1976, p232). As an information competency, it enables learners to link technology tools with a set of information skills, thus providing an intentionality that moves the focus from the technology more directly to the information. As a social competency, its underlying principles include enabling learners to understand others’ ideas, develop and follow through on shared practices, build win-win relationships, and resolve conflicts. PKM integrates human cognitive and metacognitive competency (Sheridan, 2008), social competency (Wright, 2005; Pettenati & Cigognini, 2009) and informational competency (Tsui, 2002). Wright (2007) has developed a PKM Planning Guide for developing knowledge worker PKM competency. The guide is based on his research findings that the four interrelated competencies are activated in order to plan PKM training. The training process encourages participants to reflect on their knowledge activities and focus on areas for improvement. If learners know how to control this process, they can internalize information into personal knowledge, creating a foundation for effective learning.
Utilizing PKM for acquiring knowledge refers to a collection of information management
processes that an individual learner needs to carry out in order to gather,
classify, store, search, and
retrieve information in his daily activities (Tsui,
2002; Grundspenkis, 2007). In teacher education,
knowledge acquisition focuses on the process how teacher apply PKM to support
their day-to-day teaching and learning activities: instructional design.
Instructional design is closely related to PKM which is also one of the major
learning tasks for pre-service teachers. Instructional design is a process that
involves determining the current status and needs of
the learner, defining the end goal of instruction, and creating instructional
and learning strategies to facilitate teaching and learning. There are a wide range of instructional design models, many of
them based on the ADDIE model (Seels & Glasgow,
1998; Molenda, M., 2003; Strickland, A.W. 2006) which
includes the following phases: analysis, design, development, implementation,
and evaluation. This acronym stands for the 5 phases contained in the model.
Knowledge acquisition for instructional design is conceptualized as identifying learner entry skills, formulating instructional
objectives, test and design specifications, creating
instructional or training materials, making recommendations and preparing a
project report for lesson implementation.
As instructional design is one of the key components of teacher
professional competence, and helps to implement a new curriculum in the
information age of the 21st century, exploring the predictive
relationships of PKM competency on knowledge acquisition for instructional
design becomes key to the development of teacher
education.
Research
Methodology
It appears that PKM competency can expand
individuals’ knowledge and enhance their learning competency (Davenport, 1997, p146 ; Frand & Hixon, 1999).
It provides learners
with a targeted, reflective and adaptable cognitive framework for inquiry and
problem solving. In this study, knowledge acquisition will be conceptualized
as the knowledge required for carrying out instructional design. This
study attempts to answer the following research questions:
1.
What is the empirical factor structure of PKM competency for pre-service
teachers?
2.
Is there any
relationship between the PKM competency of pre-service teachers and their
knowledge acquisition
for instructional design?
This study
adopted Dorsey (2000) PKM skills to conceptualize PKM as a competency for
acquiring knowledge (see figure 1). A quasi-experimental research design was
used in this study to determine the relationship between PKM skills and
knowledge acquisition for instructional design. The exogenous
variables were pre-service teachers' perceptions of their PKM skills. The endogenous variable is
knowledge acquisition for instructional design. A self-response
quantitative questionnaire was devised to collect data from the pre-service
teachers of
Figure 1: Theoretical Framework Of The Study
The operationalized definitions of Dorsey (2000) PKM skills are as follows:
1. Retrieving skill is the ability of learners to retrieve information from relational databases, electronic library databases, websites, threaded discussion groups, recorded chats, and moderated and unmoderated lists.
2.
Evaluating skill is the ability to make judgments on both the quality and
relevance of information to be
retrieved, organized, and analyzed.
3. Organizing skill is the ability to
make the information one’s own by applying ordering and
connecting principles that relate new information to old information.
4. Collaborating skill is the ability to understand others’ ideas, develop and follow through on shared practices, build win-win relationships, and resolve conflicts between these underlying principles.
5. Analyzing skill is the ability to extract meaning from data and convert information into knowledge.
6.
Presenting
skill is the ability to familiarize with the work of communications
specialists, graphic designers, and editors.
7. Securing skill is the ability to develop and implement practices that help to ensure the confidentiality, integrity and actual existence of information.
This study adopted ADDIE instructional design model to conceptualize instructional design as a multiple competencies that involves analysis, design, development, implementation, and evaluation of a lesson (Molenda, 2003; Strickland, 2006). The acronym ADDIE stands for the 5 phases contained in the model. Pre-service teachers’ learning on instructional design is conceptualized by the knowledge and experiences they come across in the 5 phases of ADDIE model including analysis, design, development, implementation and evaluation. The learning outcomes include know how to analyse learner characteristics and task to be learned and identify learner entry skills; to design learning objectives and choose an instructional approach; to develop instructional or training materials; implement the lesson and deliver the instructional materials; and to evaluate the lesson plan and recommend the materials achieved the desired goals. The teaching experience that they had gained include determining the current state and needs of the learner, defining the end goal of instruction, and creating some instructional and learning strategies to facilitate teaching and learning. Instructional design is operationalized to the knowledge for:
· identifying learner entry skills;
· formulating instructional obJectives, test and designs specifications;
· creating instructional or training materials; and
·
making recommendations and preparing a project report for
lesson implementation (Seels & Glasgow, 1998; Molenda, M., 2003; Strickland, A.W. 2006).
The questionnaire was based on a number of scales constructed to measure the variables of PKM skills and instructional design. In order to develop valid items for these scales, the researcher conducted a content analysis on the PKM literature of Dorsey (2000), Skyrme (1999). Hyams (2000), and on the instruction design literature of Seels & Glasgow (1998), Molenda, M. (2003); and Strickland, A.W. (2006). The questionnaire consists of two sections. Section 1 was used to measure the effectiveness of knowledge acquisition for instructional design based on 4 items. Section 2 contains 21 items designed to measure the teachers’ perceptions of their seven PKM skills. Likert 6 point scales were used in both sections to measure the variables. Likert scales are commonly used in attitudinal research. The Likert scale assumes that the difference between answering 'agree strongly, and 'agree' is the same as between answering 'agree' and 'neither agree nor disagree' (Likert 1932, quoted in Gay, 1992). The data was collected directly from target subjects using the questionnaire.
225
pre-service teachers responded to the survey. Data was collected directly from
them by means of the questionnaire. The subjects in the
study were pre-service teachers from Hong Kong’s largest teacher
education institution. Random sampling was used to collect data
from the population. Exploratory factor analysis was carried out
on variables using principal factor axis analysis to confirm the constructed
validity of the tools (see table 1). The study is interested in a theoretical
solution uncontaminated by unique and error variability and is designed with a
framework on the basis of underlying constructs that are expected to produce
sources on the observed variables. Principal axis factor (PAF) analysis, which
aims to reveal the underlying factors that produce the correlation or
correlations among a set of indicators with the assumption of an implicit
underlying factor model, was applied separately to the items from the learning
processes and learning outcomes. Promax rotation, a
method of oblique rotation which assumes that the resulting factors are
correlated with one other, was applied to extract the factors. An eigenvalue greater than one was used
to determine the appropriate number of factors for the factor solutions.
A Structural Equation Model (
Findings
The results of exploratory factor analysis, presented in Table 1, clearly suggest a four-
factor structure for exogenous variables that are both empirically
feasible and theoretically acceptable. An eigenvalue greater than one was used to
determine the appropriate number of factors for the factor analysis solution. Items were extracted with factor loadings greater than 0.6 across and within
factors. The numbers of factor solutions extracted from a Promax rotation theoretically afforded the most
meaningful interpretation. The process used to identify and label the factors
that emerged was based on examining the derivation of the highest loading items
on each of the factors. The reliability coefficients of the
scales ranged from 0.792-0.821,
which was judged adequate for this study. The results of descriptive
statistics show that the scale means of all
the variables are higher than 4.27
within the 6 point-scale, reflecting the participants’ tendency to slightly
agree with all the items. The reliability coefficient (Alphas) of the scale for
instructional design is 0.854, its scale mean is 4.33 (sd
= 0.691).
Table 1: Results
of Exploratory Factor Analysis
Dimension Item Factor 1 Factor 2 Factor 3 Factor 4 Collaborating Q23 I can share relevant information with other team
members for completion of the team tasks. .890 Q25 I know how to share information with team
members to enhance team working effectiveness. .738 Analyzing Q15 I can use MS Excel for statistical data
analysis. .769 Q16 I can interpret the hidden meaning of research information. .757 Organizing Q11 I use ordering and connecting principles that
relate new information to old information. .839 Q12 I connect and organize information using electronic
tools such as directories and folders, databases, web pages, and web
portals. .825 Q13 I always synthesize and analyze information. .456 Retrieving Q6 I never search the
internet without targets. .822 Q7 I know how to
retrieve the teaching material for my subject effectively. .702 Eigenvalue 7.599 1.500 1.058 1.001 % of Variance Explained 42.21% 8.33% 5.88% 5.55% Reliability
coefficients (Alphas) 0.821 0.789 0.796 0.792 Mean 4.35 4.49 4.27 4.45 SD 0.648 0.612 0.664 0.640 Instructional Design Q1 Identify
learner entry skills Q2 Formulate
instructional objectives Q3 Create instructional
or training materials Q4 Make
recommendations for the lesson implementation Reliability
coefficients (Alphas) = 0.854, Scale mean = 4.33, SD = 0.691
The structural and measurement coefficients from the
completely standardized solution under maximum likelihood are presented in
Figure 2. The goodness of fit statistics are shown in
Table 2. All the paths in the model were significant at the 0.05 level according
to the Z statistics. A PKM Model for pre-service
teachers was empirically confirmed by the
Table 2: Goodness Of Fit Statistics Of The
Structural Equation Model
c2 df p-value RMSEA SRMR NNFI IFI 70.6 59 0.1433 0.031 0.044 0.99 0.98 0.99
The hypothesized model is a good fit to the data.
The results of the LISREL with 225 participants showed that the chi square
value was not significant for the overall model, c2 (N=225) = 70.6, P
= 0.1433. As an absolute fit index, the chi square assesses the discrepancy
between the sample covariance matrix and the implied covariance matrix based on
the hypothesized model. A non-significant chi-square suggests that the model
may be a reasonable representation of the data. However, the assessment of fit
using the chi square test is confounded by sample size. When the sample size is
large, the small difference between the sample covariance matrix and the
reproduction covariance may be found to be significant.
Relative-fit index and residual-based indexes are
two types of additional fit indexes widely used to complement chi-square.
Relative-fit indexes include comparative fit index (
In addition to relative-fit indexes,
residual-based indexes can also be used. Standardized root means square (SRMS)
measures the average value across all standardized residuals between the
elements of the observed and implied covariance matrices. Root mean square
error of approximation (RMSEA) assesses the absence of fit because of model
misspecification and provides a measure of discrepancy per degree of freedom
(Browne & Cudeck, 1993). SRMR range from zero to
one and there is no upper limit for RMSEA, with smaller values indicating a
better model fit. A value of 0.08 or less for SRMR and a value of 0.06 or less
for RMSEA indicate an adequate fit (Hu & Bentler, 1999). In this study, SRMR = 0.044, while RMSEA =
0.031 (90% CI. 0.0; 0.053). Given that this is a very stringent model in which
the correlations among all measurement errors were not set free, these fit
statistics indexes show that the model fit the data fairly well.
Regarding the
first research question, the empirical model clearly shows that retrieving,
organizing, analyzing and collaborating skills are all involved in PKM competency. Participants in this study tend to slightly agree that they are able
to retrieve teaching materials
via the
internet (mean = 4.45, sd = 0.64), organize the information themselves by using
ordering and connecting principles that relate new information to old
information (mean = 4.27, sd = 0.66), extract meaning from data and
convert information into knowledge (mean = 4.49, sd =
0.61) and understand others’ ideas, develop and follow through on
shared practices (mean = 4.35, sd =
0.648).
Surprisingly, the model does not include the skills of
evaluating, presenting and securing information. This finding may reflect the
possibility that the pre-service teachers in this study were not aware of
exercising these skills when acquiring knowledge or that the use of these
skills is actually outside the knowledge acquisition process. Evaluation of
information can take place as part of the information retrieval process (Frand & Hixon, 1999);
therefore evaluating skills may be embedded in the retrieving skills,
especially during the information search process. Conceptually, presentation
skills and information securing skills could be applied theoretically for managing
information, but they are closely related to the aspects of knowledge
dissemination and knowledge protection respectively, both of which skills are
commonly applied after the information and knowledge conversion process.
Therefore, presenting skills and securing skills should arguably be part of the
knowledge creation process.
Regarding the second research question, the results show that there is a
predictive relationship between PKM competency and knowledge acquisition for
instructional design (γ = 0.19). The
results reflect participants’ ability to exercise their
retrieving, organizing, analyzing and collaborating skills for instructional design (mean =
4.33, sd = 0.691 ). They can order and
connect retrieved information for analysis, and convert the information
to knowledge via individual or collective analysis. This finding is consistent with the studies
of Wright, 2005, Tsui, 2002 and Grundspenkis,
2007, in which learners can apply PKM competency to support their day-to-day
learning activities. The findings of this study suggest that enhancing the PKM
competency of pre-service teachers can enhance
their knowledge acquisition for instructional design.
Another interesting finding emerges when
comparing the knowledge
acquisition process using retrieving,
organizing, analyzing and collaborating skills with Nonanka and Takeuchi’s (1995) knowledge
creation model. Nonanka and Takeuchi describe
how knowledge conversion takes place through an iterative and spiral process of
socialization, externalization, combination and internalization as an effective
means of making
individual knowledge available to the broader organization in order to create
new knowledge. The empirical PKM model of the study focuses on manipulation of
an intertwined macro-competency for knowledge acquisition at individual level in which the
individual learner organizes retrieved information for conversion into
knowledge and then shares it with others (see figure 3). It is similar to the
knowledge creation process in Nonanka and Takeuchi’s
(1995) model, in which a learner retrieves information from knowledge sharing,
then combines it with existing information and internalizes it as his own
knowledge.
Discussion
A set of learning outcomes for planning the PKM curriculum could be
articulated from the extracted items of the four PKM elements. For example,
pre-service teachers should be able to access databases and websites for
information retrieval; operate electronic tools for information integration; use
spreadsheet and statistical software for data and information analysis; use collaborative PKM tools for
collaboration to support both synchronous and asynchronous communication for
the purpose of learning; and construct
knowledge that is based on an appropriate understanding of the nature of data,
sound inference, and an understanding of potentially meaningful relationships
within a data set. To develop
pre-service teachers on the basis of the above derived learning outcomes,
competency training including the four interrelated
skills of cognitive and metacognitive, information, social and learning should be
provided (Wright, 2005). If teacher education
institutions really want to fully engage pre-service teachers with a
professional and lifelong learning process, they should develop pre-service
teachers’ PKM competency by making PKM tools available (Tsui,
2002), providing training through e-learning activities (Pettenati
and Cigognini, 2009) and conducting collaborative
action research (Zuber-Skerritt, 2005; Cheng,
2009).
Tsui
(2002) takes a technology-centric view of PKM and looks at the challenges and
problems associated with the use of PKM tools. He considers PKM as a set of information skills and describes several
categories of tools for developing PKM skills. These PKM tools are search/index
tools, meta-search tools, information capture and sharing tools, associative
link tools and concept/mind mapping tools, email management, voice recognition,
collaboration and synchronization and learning tools. Garner (2010) proposes
using wikis to support and develop PKM skill. A wiki is a web application whose
content is collaboratively added to, updated, and organized by its users
(Mitchell, 2009), and which can be utilized in knowledge management within
education to support analysis and collaboration around information. Learners
can acquire relevant new knowledge by internalizing information from a wiki.
Besides efficient use of PKM tools, e-learning activities also involve
sharing and intelligent practices that guide the use of tools. e-learning is a means of learning that uses wireless mobile
communications network technology and wireless mobile communication systems,
individual digital assistants, etc. to access information and resources. Pettenati and Cigognini (2009)
devised a conceptual model on e-learning activities to develop adult learner
PKM skills. These activities involve using internet tools for teaching PKM
skills. The training is built around learning purposes and activity tasks, and
requires learners to respond to, comment on and evaluate others’ learning. This
training model involves the development of cognitive, metacognitive
and information skills.
E-learning activities should be delivered by action research approach. Action research is a form of self-reflective enquiry undertaken by participants in educational situations in order to improve the rationality and justice of their own educational practices, their understanding of these practices and the situations in which the practices are carried out (Kemmis, 1988). Studies involving teachers in collaborative action research into their own practices can be traced back to John Elliott’s research work (1976). As part of the action research process, teachers are expected to learn cooperatively and become reflective practitioners (Schon, 1983) by practising theories postulated from others. Research shows that incorporating action research approaches into initial teacher education programmes could educate reflective teachers to deal with the complexity of practice, but that adequate resources and support need to be provided for the programme implementation (Gore & Zeichner, 1991; Price, 2001; Cochran-Smith, 2004; Mills, 2007).
Zuber-Skerritt (2005) proposes a model of action research
and action learning to help knowledge workers access,
communicate and manage personal knowledge. This soft approach could help
develop people’s PKM competency. Pre-service teachers appreciate collaborative
action learning and value opportunities for deliberation and reflection on
experience (Eisner, 2002) as long as they feel confident speaking about their
experiences of knowledge acquisition. Cheng (2009) adopted a CoP framework to help a group of five in-service teachers
create pedagogical content knowledge for mathematics teaching. He applied an
action research approach titled Learning Study to cultivate and facilitate a
community of practice for studying the knowledge sharing and creation process.
He discovered that the collaborative action research approach can develop
teachers’ learning competency for knowledge creation.
To support the sustainable development of teachers as professionals in
the knowledge society, teacher education institutions should integrate PKM
tools, e-learning activities and collaborative action research into the
pre-service teacher education curriculum. This could be of significant
assistance to pre-service teachers in retrieving, organizing, analyzing and
collaborating around information across all disciplines. If PKM skills are
taught, acquired and utilized in each discipline across the curriculum,
teachers can organize and integrate information to provide strategies for
transforming what might be random pieces of information into something that can
be systematically applied and that expands their personal knowledge. Nurturing
pre-service teachers with PKM competency can help to sustain a competitive
human capital in the knowledge economy.
Limitations
Of The Study
Several limitations of this study should be noted. One important issue is the generalizability of this study. Although the questionnaire appears to have constructive content and validity in addition to relatively high reliability, the fact that all the pre-service teachers were from a single institution means that, the findings of this study may have limitations in terms of general application to other populations. As far as the predictive validity of the findings is concerned, although the data was collected from self-response questionnaires posited as evidence of the four PKM skills, the researcher is not certain whether the participants perceived the competencies found in this study as actual long-lasting competencies that can be transferred to other instructional situations.
Implications For Future Research
It
is evident from the study’s findings and conclusions that additional research
is necessary. Firstly, in-depth qualitative research is needed to
triangulate the findings of this study. This could provide a better
understanding of why and how pre-service teachers’ PKM competency skills can enhance their
knowledge acquisition. It would explore
how PKM
skills support the process of converting
information into knowledge. It might also explore the
learning process experienced by pre-service teachers exercising their PKM
skills. Secondly,
this study was restricted to a single
Conclusion
This study constructs an empirical model for articulating the personal knowledge management competency of pre-service teachers. The PKM competency model for pre-service teachers is identified as a four-factor structure which consists of retrieving, organizing, analyzing and collaborating skills. Enhancing pre-service teacher PKM competency can improve their knowledge acquisition in teaching. It contributes to existing PKM literature by providing an empirical pre-service teacher PKM model, and to existing teacher education by providing a PKM curriculum framework. It is in the interests of teacher education institutions to inject PKM elements into their teacher education curriculum, if they are serious about the competency of future teachers to develop primary and secondary school students’ learn to learn skills to help them cope with the demands of recent curriculum reform. Teacher education institutions should consider facilitating pre-service teachers’ cognitive, metacognitive, learning, information and social competency by providing e-learning activities on the use of PKM tools and collaborative action research throughout their training programme. This PKM model may enhance the ability of pre-service teachers to learn how to learn and to adapt to change and provide a framework to support lifelong learning and the sustainable development of teachers as professionals.
References
Avery,
S., Brooks, R., Brown, J., Dorsey, P. & O’Conner, M. (2001).
Personal knowledge management:
Framework for integration and partnerships. Retrieved October 1, 2009, from http://www.millikin.edu/pkm/pkm_ascue.html
Browne, M. W. & Cudeck,
R. (1993), “Alternative
ways of assessing model fit”. In K. A. Bollen, J. & Long, J.S. (Eds.), Testing structure equation models, Sage,
Cheng, C. K. (2009). Cultivating communities of practice via Learning Study for enhancing teacher learning, KEDI Journal of Educational Policy, 6(1), 81-104.
Cochran-Smith, M. (2004). Walking the road: race,
diversity, and social justice in teacher education.
Cochran-Smith,
M., & Lytle, S. (1999). Relationships of knowledge and practice: Teacher learning in community. In the series, Review of
Research in Education, 24, 249-305.
Dorsey, P. A. (2000). Personal knowledge management: educational framework for global business. Retrieved October 1, 2009, from http://www.millikin.edu/pkm/pkm_istanbul.html
Education Commission (2000). Review of education system-reform proposals
(consultation document).
EMB (Education and Manpower Bureau HKSAR).
(2004). Information
Technology in Education - Way Forward.
http://www.edb.gov.hk/FileManager/EN/Content_93/ite_way_forward_e.pdf
EMB (Education and Manpower Bureau HKSAR).
(1998). Information Technology for Learning
in a New Era: Five-year strategy 1998/99 to 2002/03.
Eisner, E. W. (2002). From episteme to phronesis to artistry in the study and improvement of teaching. Teaching & Teacher Education, 18, 375–385.
Elliott, J. (1976). Developing hypotheses about classrooms from teachers’ practical constructs: an account of work of Ford Teaching Project. Interchange 7 (2), 2-22.
Flavell, J. H. (1976). Metacognitive aspects of problem solving.
In L. B. Resnick (Ed.), The
nature of intelligence (pp. 231–236).
Frand, J. & Hixon, C. (1999). Personal Knowledge Management: Who, What, Why, When, Where, How? Working paper. Retrieved December 1, 1999. http://www.anderson.ucla.edu/jason.frand/researcher/speeches/PKM.htm.
Garner, S. (2010 June, 19). Supporting
the personal knowledge management of students
with technology. Paper presented at the Informing
Science & IT Education Conference (InSITE) 2010,
Gay, L. R. (1992). Educational Research: Competencies for
Analysis and Application.
Gore, J. M., & Zeichner, K. M.
(1991). Action research and reflective teaching in pre-service teacher
education: A case study from the
Grundspenkis, J. (2007). Agent based approach for organization and personal knowledge modelling: knowledge management perspective. Journal of Intelligent Manufacturing 18 (4): 451-457.
Hu, L. & Bentler, P. M. (1999). Cutoff Criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternative: Structural Equation Modelling 6(1), 1-55.
Hyams R. 2000. 10 Skills of
personal knowledgemanagement. Retrieved October, 10,
2008. from www.cgn.com/html/main.html?ContentFrame=http://www.cgn.com/html/services/
knowledge /personal.html.
Jöreskog K. G.,
& Sörbom D. (1999). LISREL 8.3: User’s reference guide.
Kemmis, S. (1988). Action research.
In J. P. Keeves (ed), Educational Research methodology
and measurement: An international Handbook (pp.237-53). Pergamon:
Mills, G. E. (2007). Action
research: A guide for the teacher researcher.
Mitchell, S. (2009). Personal easy wiki hosting, Scott Hanselman's blog, and snagging screens. Retrieved 10/12/2009 from http://msdn.microsoft.com/en-us/magazine/cc700339.aspx
Molenda, M. (2003). In search of the elusive addie model. Performance Improvement, 42(5), 34.
Pettenati,
Price, J. (2001). Action research, pedagogy and change: The transformative potential of action research in pre-service teacher education. Journal of Curriculum Studies, 33(1), 43-74.
Schon, D. A. (1983). The
reflective practitioner: How professional think in action.
Seels, B.
& Glasgow, Z. (1998). Making instructional design decisions.
Skyrme, D.J., (1999). The knowledge Networker’s Took kit. Butterworth: Heimann.
Strickland, A.W. (2006). ADDIE.
Tsui, E. (2002). Technologies for personal and peer-to-peer (P2P) knowledge management. A Computer Sciences Corporation Leading Edge Forum report. Retrieved September 30, 2010 from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.84.9689 1-53.
Wright, K. (2005). Personal knowledge management: supporting individual knowledge worker performance. Knowledge Management, Research and Practice, 3, 156–165.
Wright, K. (2007). Personal knowledge management planning guide - Developing ways to work smarter not harder?" Retrieved September 30, 2010, from http://www.knowledgeresources.ca/Knowledge_Resources/PKM_Planning_files/PKM%20Planning%20Guide.pdf
Zuber-Skerritt, O. (2005). A model of values and actions for personal
knowledge management. Journal
of Workplace Learning, 17(1/2), 49-64.
Contact the Author:
Eric Cheng, Assistant Professor,
The Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, NT, Hong Kong
Email: eckcheng@ied.edu.hk: Phone: 852-29488478