Open-ended digital learning tasks Reading resources used in the study Saved documents open-ended digital learning tasks & presentations

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Introduction

Schools in Tanzania and around the globe are scrambling to make sure that learners do not end up on the wrong side of the digital divide (Nelson, Post & Bickel, 2003). As a result of pressure from politicians and parents and because of the imperatives of the contemporary working environment, public schools are purchasing as much computer hardware, software and Internet connectivity as they can afford to create the kind of upto-date “wired” or “high tech” environment that learners need for their training in computer and other digital technologies (Nelson et al., 2003). What has become evident is that even in those cases where schools have been able to purchase the latest technology and provide the kind of training that such technology requires, the quality of instruction and learner participation in learning and achievement have not necessarily shown improvement (Nelson et al., 2003). Why is this the case? The answer may be located in the fact that the technology used in such environments has been utilised and implemented in ways that have undermined meaningful learning (Newmann & Wahlage, 1993). To support and encourage meaningful learning, teachers need to select models of instructional design that might incorporate methods that encourage the use of activity teaching methods and problem solving approaches that also encourage learners to participate actively as they acquire practical experience of the various forms of technology with which they interact with (Computer Studies Syllabus of Tanzania, 1996, p. iii).

ICT status in the education system in Tanzania

Tanzania has made a remarkable progress in deploying educational Information and Communication Technologies (ICT) so as to redress unmet demands and competition in the newly liberalised markets (National ICT Policy, 2003). In the learning context however, very few educational institutions have computer laboratories or other multimedia facilities that can be used for teaching computer application skills to the majority of learners (Kafanabo, 1999). Such facilities tend to be found in private schools rather than in public schools. At universities and other higher learning institution the situation is similar: few computers are available for use by learners and the academic staff. Those that are available are too few to meet the demand for access and use. In addition, their scarcity creates numerous problems with regard to accessibility of facilities and their possible use in teaching and learning processes (National ICT Policy, 2003, p. 4).

Science education in Tanzania – the current situation

This section deals with the current situation of science teaching in Tanzanian schools. It is common knowledge that the teaching and learning of sciences has deteriorated in many schools. There is among many learners a profound aversion to science and its sister subject, mathematics. This common knowledge motivated me to devise an instructional method that can be used to improve the teaching and learning of science using computers in Tanzanian schools. The method that I developed comprised, in the main, open-ended (Zevenbergen, Sullivan and Mousley, 2001; Goodnough, 2003) digital learning tasks that I applied to selected topics from the Biology syllabus. This material can be readily integrated with the teaching and learning of computer application skills. The current teaching and learning of sciences in Tanzania, changes in format as learner’s progress upwards through the different grades. In primary schools for example, learners are taught what is called general science, which is a combination of biology, physics and chemistry and it suits these levels. These science subjects are not taught in-depth, and usually learners are only acquainted with the basic concepts of each subject. When the learners reach the O-level grades or the junior secondary school in Tanzania, the science subjects are differentiated into biology, physics, and chemistry, and are then taught as different subjects.

History of human intelligence and measurement procedures

The process of learning is always associated with intelligence. Different researchers have been trying to get the right definition of intelligence and identify the different components of intelligence in relation to learning. Traditionally, intelligence has been measured through intelligence tests and scales as shown in table 2.1. It has never been easy in understanding the nature of human intelligence and devising methods to assess it, and ever since, it has been the central problem in psychology since its inception. Hence, the definition of intelligence and measurement methods has been changing ever since with the aim of getting the right instrument to measure intelligence. Table 2.1 below shows the different intelligences and how these intelligences were measured in the modern definition of intelligence.

Definition of intelligence in the theory of multiple intelligences

In Gardner’s classic work, Frames of Mind: The Theory of Multiple Intelligences (Gardner, 1983), defines intelligence generally as ‘the capacity to respond successfully to new situations – to tackle a task demanded by life’ (p.8). Gardner (1999b) elaborates on this general definition by further defining intelligence in Intelligence Reframed: Multiple Intelligences for the 21st Century as ‘a bio-psychological potential to process information in a cultural setting to solve problems or create products that are of value in at least one culture’ (pp. 33-34). In these two definitions, as elaborated in these texts, Gardner asserts that intelligence is pluralistic and that it can be located in at least seven intelligences which he lists as: verbal linguistic, logic mathematical, musical, bodily-kinaesthetic, visual spatial, interpersonal, and intrapersonal. In the second book work quoted above, Gardner (1999b) added three other intelligences to the seven intelligences mentioned above. These are naturalistic, moral intelligence and existential intelligences. Gilman (2001) notes that Gardner is comfortable with declaring that a naturalistic intelligence meets the criteria that he has set himself, he is less sure about how to define and incorporate moral and existential intelligences. Naturalistic intelligence for Gardner conforms to the criteria of existence as intelligence. He therefore adds it to his list and ends up with eight kinds of intelligences. It is important for Gardner’s plausibility to note that the majority of existing empirical research and available measurement tools, including Teele Inventory of Multiple Intelligences (TIMI) developed by Sue Teele (1992) and Multiple Intelligences Developmental Assessment Scales (MIDAS) developed by Brandon Shearer (1997), are based on Gardner’s original theory of seven multiple intelligences.

Table of Contents :

• Chapter 1 – Introduction 1.1 Introduction
  • 1.2 Aim of the study
  • 1.3 Background of the study
  • 1.3.1 Current use of technology in schools
  • 1.3.2 Why are schools not using computers effectively
  • 1.4 Tanzanian context
  • 1.4.1 ICT status in the education system in Tanzania
  • 1.4.2 Introduction of computers in Tanzanian secondary schools
  • 1.4.3 Why has the use of technology failed to improve the quality of instruction or learner achievement?
  • 1.4.4 Science education in Tanzania – the current situation
  • 1.4.5 Current practices of assessment in computer studies in Tanzania
  • 1.5 Purpose of the study
  • 1.6 Statement of the problem
  • 1.7 Critical research question
  • 1.8 Design of the study
  • 1.9 Significance of the study
  • 1.10 Delimitation of the study
  • 1.11 Limitation of the study
  • 1.12 Preview of the study
• Chapter 2 – Literature Review
  • 2.1 Introduction
  • 2.2 History of human intelligence and measurement procedures
  • 2.3 Theoretical framework
  • 2.3.1 Theory of multiple intelligences
  • 2.3.1.1 Origin of the theory –diverse sources of evidence for multiple intelligences
  • 2.3.1.2 Definition of intelligence in the theory of multiple intelligences
  • 2.3.1.3 Summary of eight intelligences and their definitions
  • 2.4 Implications of multiple intelligences in schools
  • 2.4.1 Implementation of multiple intelligences in schools through the use of projects
  • 2.5 Multiple intelligences and assessment
  • 2.5.1 Multiple intelligence theory and performance assessment
  • 2.5.1.1 Authentic context to enhance multiple intelligences
  • 2.5.1.2 Use of rubrics in assessing multiple intelligences
  • 2.5.1.3 Multiple intelligences assessment tools – MIDAS and TIMI
  • 2.5.1.4 Reliability of the instruments
  • 2.5.2 Standardized tests and their problems
  • 2.6 Multiple intelligences and technology
  • 2.6.1 How can computers in schools be effectively integrated into teaching and learning so that they reflect multiple intelligences
  • 2.6.1.1 Integrating technology and multiple intelligences
  • 2.7 Multiple intelligences – the teaching and learning of sciences
  • 2.8 Critiques of the theory of multiple intelligences
  • 2.9 Why integrate multiple intelligences in the learning process
  • 2.10 Multiple intelligences and assessment process
  • 2.10.1 Performance assessment approach
  • 2.10.2 What is performance assessment?
  • 2.10.3 Open-ended digital learning tasks and multiple intelligences
  • 2.10.4 Authentic tasks and multiple intelligences
  • 2.10.5 Scoring rubrics and multiple intelligences
  • 2.11 Multiple intelligences and learner collaboration
  • 2.11.1 Collaboration and interpersonal intelligence
  • 2.11.2 Collaboration and feedback process to learners
  • 2.11.3 Collaboration and scaffolding of learners
  • 2.12 Problems inherent in performance assessments
  • 2.12.1 Avoiding subjectivity in performance assessment
  • 2.12.2 Validity and reliability of performance assessment
  • 2.12.3 Generalizability
  • 2.13 Advantages and disadvantages of performance assessment
  • 2.13.1 Advantages
  • 2.13.2 Disadvantages
  • 2.14 Rationale for using performance assessment approach
  • 2.15 Conclusion
• Chapter 3 – Research Design and Methodology
  • 3.1 Introduction
  • 3.2 Theoretical framework – concepts on performance assessment
  • 3.3 Research design
  • 3.3.1 Research paradigm
  • 3.3.2 Data collection strategies
  • 3.3.2.1 Stage 1: Learner’s Multiple Intelligence Survey questionnaire Learners school progress report
  • 3.3.2.2 Stage 2: Open-ended digital learning tasks Reading resources used in the study Saved documents open-ended digital learning tasks & presentations
  • 3.3.2.3 Stage 3: Observation checklists
  • 3.3.2.4 Stage 4: Focus group interviews with the learners
  • Teacher interviews
  • Teacher demographic questionnaire
  • Parent interviews
  • Parent demographic questionnaire
  • 3.3.2.5 Stage 5: Assessment of learners performance abilities using scoring rubrics
  • 3.4 Research methodology
  • 3.4.1 Study profile – geographic context
  • 3.4.2 Sampling –schools, learners and teachers
  • 3.4.3 Implementation of the tasks
  • 3.5 Data analysis procedures
  • 3.5.1 Readability statistics of the open-ended digital learning tasks
  • 3.5.2 Cohen kappa statistical measure of inter-rater scores in open-ended digital learning tasks and presentation documents
  • 3.5.3 Analysis of the multiple intelligence survey questionnaire
  • 3.5.4 Analysis of learners school progress report
  • 3.5.5 The relationship between multiple intelligences and learners’ performance using a contingency table
  • 3.6 Validity and reliability of the study
  • 3.6.1 Credibility
  • 3.6.1.1 Triangulation of data
  • 3.6.1.2 Multiple investigators
  • 3.6.1.3 Avoiding research bias
  • 3.6.1.4 Thick and rich description
  • 3.6.2 Dependability/Consistency
  • 3.6.2.1 Consistency in observations
  • 3.6.2.2 Consistency in open-ended digital learning task text documents and presentation documents
  • 3.6.2. 3 Consistency in interviews
  • 3.6.3 Validity and reliability of scoring rubrics
  • 3.6.4 Transferability/ Generalizability
  • 3.7 Ethical issues
  • 3.7.1 Ethical issues in data collection
  • 3.7.1.1 Gaining access
  • 3.7.1.2.Participants’ participation
  • 3.7.1.3 Interview process
  • 3.7.1.4 Questionnaires
  • 3.7.2 Ethical issues in data analysis and interpretation
  • 3.7.2.1 Analysis of data
  • 3.7.3 Ethical issues in writing and disseminating research findings
  • 3.7.3.1 Dissemination of research findings
• Chapter 4 – Research Findings & Analysis
  • 4.1 Introduction
  • 4.2 Value of performance assessment on learning
  • 4.2.1 Computer application skills
  • 4.2.2 Preference diversity between learners
  • 4.2.3 Collaboration and interpersonal intelligence
  • 4.2.4 Social learning
  • 4.3 Value of open-ended digital learning tasks in learning
  • 4.3.1 Learners motivation to learn
  • 4.3.2 Sustained attention
  • 4.3.3 Learners’ ownership of the tasks
  • 4.4 Interaction between multiple intelligences and performance of the learners in open-ended digital learning tasks
  • 4.4.1 Intelligence profiles of the learners
  • 4.4.2 Results of the study
  • 4.4.2.1 Logic mathematical intelligence
  • 4.4.2.2 Verbal linguistic intelligence
  • 4.4.2.3 Visual spatial intelligence
  • 4.4.2.4 Interpersonal intelligence
  • 4.4.3 Conclusion
  • 4.5 The relationship between learners’ intelligence profiles and performance in computer application skills
  • 4.5.1 Assessment of learners’ performance abilities in computer application skills in relation to the three intelligences
  • 4.5.1.1 Recording, organizing and using number information (logic mathematical intelligence
  • 4.5.1.2 Visual spatial application skills – pictures, clip art, colours, tables and graphs, font size and style (visual spatial intelligence)
  • 4.5.1.3 Organization of ideas – paragraphs, bullets, and columns (verbal linguistic)
  • 4.5.2 The relationship between learners intelligence profile and computer application skills in different intelligences
  • 4.5.2.1 High /low profiles in logic mathematical intelligence and computer application skills
  • 4.5.2.2 High/ low profile in visual spatial intelligence and computer application skills
  • 4.5.2.3 High/ low profile in verbal linguistic intelligence and computer application skills
  • 4.5.3 Conclusion
  • 4.6 Learners’ intelligence profiles, preferences and performance abilities in four intelligences across the tasks
  • 4.6.1 Story 1: Strong in logic mathematical, visual spatial and verbal linguistic intelligences
  • 4.6.1.1 Logic mathematical intelligence
  • 4.6.1.2 Visual spatial intelligence
  • 4.6.1.3 Verbal linguistic intelligence
  • 4.6.1.4 Interpersonal intelligence
  • 4.6.2 Story 2: Strong in verbal linguistic intelligence
  • 4.6.2.1 Verbal linguistic
  • 4.6.2.2 Visual spatial intelligence
  • 4.6.2.3 Logic mathematical intelligence
  • 4.6.2.4 Interpersonal intelligence
  • 4.6.3 Story 3: Strong in interpersonal intelligence
  • 4.6.3.1 Interpersonal intelligence
  • 4.6.3.2 Verbal linguistic
  • 4.6.3.3 Visual spatial intelligence
  • 4.6.3.4 Logic mathematical intelligence
  • 4.6.4 Story 4: Strong in visual spatial intelligence
  • 4.6.4.1 Visual spatial intelligence
  • 4.6.4.2 Verbal linguistic intelligence
  • 4.6.4.3 Logic mathematical intelligence
  • 4.6.4.4 Interpersonal intelligence
  • 4.6.5 Conclusion
  • 4.6.5.1 Logic mathematical intelligence
  • 4.6.5.3 Verbal linguistic intelligence
  • 4.6.5.3 Visual spatial intelligence
  • 4.6.5.4 Interpersonal intelligence
  • 4.6.6 Synthesis
• Chapter 5 -Discussion, Conclusion & Recommendations
  • 5.1 Summary of the study
  • 5.1.1 Rationale
  • 5.1.2 Design of the study
  • 5.1.3 Methodological reflections
  • 5.2 Discussion of the results
  • 5.2.1 Multiple intelligences and learners’ intelligence profiles
  • 5.2.2 Digital tasks and the performance of the learners in computer application skills
  • 5.2.3 Learner-centeredness and authentic tasks
  • 5.2.4 Performance assessment of open-ended digital learning tasks
  • 5.2.5 Teachers performances
  • 5.3 Scientific reflection
  • 5.3.1 Contributions of this study
  • 5.4 Conclusions of the study
  • 5.4.1 Theory of multiple intelligences
  • 5.4.2 Open-ended authentic tasks
  • 5.4.3 Varied performance profiles and preferences
  • 5.5 Recommendations
  • 5.5.1 Recommendations for policy and practice
  • 5.5.2 Recommendations for examinations and assessment institutions
  • 5.5.3 Recommendations for future research
  • 5.6 Final conclusion
  • References

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An investigation into the interaction between multiple intelligences and the performance of learners’ in open-ended digital learning tasks.