A Study Of The Predictive Effect Of Pre-Service Teacher
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 Hong Kong’s largest teacher education institution. A structural equation model was applied to explore the predictive power of PKM competency on their instructional design. Results show that a four-factor PKM competency model, which consists of retrieving, analyzing, organizing and collaborative skills, was identified as a predictor of instructional design. Use of PKM tools, e-learning activities and collaborative action research for developing pre-service teacher PKM competency are recommended to teaching education institute.
Keywords: Personal knowledge management, Pre-service teacher, Teacher education
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 Hong Kong (Education Commission, 2000) addressed this lifelong education issue by proposing a learning to learn slogan in the policy document. The policy suggests that teachers should develop student learning competence for acquiring knowledge through various methods. To develop students with knowledge acquisition skills, teachers should also be equipped with the competency for knowledge acquisition. However, since publication of the policy paper entitled Information Technology for Learning in a New Era Five-year Strategy that launched IT in education in Hong Kong (EMB, 1998), the Education Bureau has not addressed this issue in any teacher professional development policy documents. Recent calls for consultation on e-learning from the Education Bureau likewise generated additional demand for developing teacher information literacy capable of supporting student learning (EMB, 2004).
If the government and teacher education institutions really want to develop competent teachers for the knowledge society, they may consider injecting the elements of personal knowledge management (PKM) into the teacher education curriculum for developing pre-service teachers’ teaching competency. However, little studies on teacher education were attempted to examine the effect of PKM on teacher learning and discussed the possibilities of injecting the element of PKM model into teacher education curriculum. This study aims to construct an empirical model for examining the predictive effect of pre-service teachers’ PKM competency on their instructional design skills and to discuss a personal knowledge management curriculum framework for teacher education institutions.
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 andmetacognitive, 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.
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 Hong Kong’s largest teacher education institution.
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 andunmoderated 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 (SEM) was then applied to examine the factor structures and the paths among the variables, using Lisrel 8.3 (Joreskog & Sorbom, 1999). SEM is a collection of statistical techniques that allows the examination of a set of relationships between exogenous variables and endogenous variables.
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 eigenvaluegreater 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).