Tokode, Olukayode
(2017)
Collaborative knowledge construction in problem-based learning: a corpus-based study.
PhD thesis, University of Nottingham.
Abstract
Background
Effective disease diagnosis and treatment relies on a conceptual knowledge base that is both expansive and well-networked. The problem-based learning (PBL) curriculum is considered as being well-suited to creating this kind of knowledge. The facilitator plays a crucial role in establishing and maintaining the knowledge construction discourse as students interact to resolve case problems. An exploration of tutorial talk could provide opportunities to understand and improve verbal interactions of this nature. Many of the previous studies have only analysed a small amount of tutorial talks owing to methodological constraints, and the existing literature on the subject matter only scarcely touches upon the utility of lexicogrammatical methods for the development of an understanding of knowledge construction in medical PBL tutorials. In this research, a blend of corpus linguistics methodology and a lexicogrammatical approach was employed for the analysis of talk in 8 PBL tutorial groups in order to deepen our understanding of how students jointly construct knowledge and how the facilitator guides the process.
Aims
In this study, a corpus of 2,37,820 comprising eight PBL students’ and facilitators’ tutorial talk was created to achieve the following aims:
I. To use the students’ subcorpus to answer the research question (1) by measuring the frequencies and describing the functions of the frequently occurring (1) referring expression indicators; (2) shared knowledge indicators; (3) knowledge extension indicators; and (4) knowledge enhancement indicators.
II. To use the facilitators’ subcorpus to answer the research question (2) by measuring the frequencies and describing the functions of the commonly occurring (1) facilitators’ questions; (2) facilitators’ directive expression indicators; and (3) facilitators’ probability indicators.
III: To make recommendations based on the results of the study.
Methodology
Wmatrix 3 was used to retrieve defined linguistic indicators relating to the research questions. A quantitative analysis of the indicators was performed through word frequency computation and a keyword-in-context analysis. Descriptive statistics with SPSS version 22 was used to computer frequency profile of the indicator functions, and the Log likelihood calculator was used to determine the variation of the functions across the eight PBL groups. Extracts from the dataset were provided to illustrate the indicators’ functions.
I. Results of Students’ talk analysis
The subcorpus contained 2,10,077 words. The most frequent contents of the students’ talk comprised biomedical science and cause-effect vocabularies.
1. Analysis of referring indicators
There were 2,325 referring expression indicators. They were used to mark verbal expressions, amounting to 44.04%; mental expressions, amounting to 42.24%; and learning situation and materials, amounting to 13.72%. The referring expressions were used for providing peer commendation, sharing knowledge, fostering social and cognitive regulation, and for constructing knowledge; the mental referring expressions were used to generate hypotheses, achieve mutual understanding, and define group tasks; and learning referring expressions were used to share learning resources, explain concepts, as well as guide discussions and resolve conflicts.
2. Analysis of shared knowledge indicators
There were 3,437 shared knowledge expression indicators, which are the following: affirmation (73%), negation (17%), and non-lexical content (10%). Affirmative indicators were mostly used for integration-oriented knowledge sharing (42.31%); negation affirmation expressions were mostly used for conflict-oriented knowledge sharing (70%); and non-content indicators were mainly used for idea and information orientation. Shared knowledge was commonly achieved among group members through information addition, repetition and rephrasing, paraphrasing, causal and noncausal elaboration, correction of ideas and information recollection, and by establishing orientation to ideas and information from the group members.
3. Analysis of knowledge extension indicators
There were 6,520 retrieved knowledge extension indicators, which comprised the following: additive 4,227 (63.54%), alternative 1,001 (15.05%), and adversative 1,424 (21.41%). Adversative indicators were more frequently used for knowledge construction compared to additive (33% versus 16%; LL 32.58, p < 0.01) and alternative indicators (33% versus 13%; LL 95.74, p < 0.01).
The students commonly used additive indicators for simple, temporal, causal-conditional, elaborate, contrastive, and indefinite additions. Alternative indicators were commonly used for offering alternative questions and ideas while adversative indicators were frequently used to link elaborative, contrastive, concessional, and causal-conditional clauses.
4. Analysis of knowledge enhancement indicators
A total of 6,402 indicators were retrieved. The most frequent among the retrieved 6,402 indicators were because, so, as, when, and that. Between 16.94% and 29.24% of the indicators were used for knowledge co-construction. The most frequent indicators’ functions were conditional, extension, report, consequence, inference, and feature specification. The reporting functions regularly concerned biomedical theory, previous peer knowledge, research evidence, professional opinion, as well as cognitive tools and criticism; extension function related to biomedical knowledge; and feature specification functions involved biomedical attributes and explanation; the conditional functions were frequently used to state logical conditions for disease presence, manifestation, and treatments; the inferential functions were more consistently used to link biomedical deductions to their premise; and the consequential functions commonly related to the linking of physiological mechanism and organ function to their respective consequences.
II: Results of facilitators’ talk analysis
The subcorpus contained 27,743 words. The most frequent content comprised biomedical science and cause-effect vocabularies.
1. Facilitators’ questions
There were 35 types of question indicators. The facilitators asked 0.78 lower-order questions per 100 tokens, and 0.25 higher-order questions problem-based per 100 tokens. The questions functioned to stimulate elaboration, elicit information, prompt students, and the offering of suggestions.
2. Analysis of directive expressions
‘Should’, ‘have to’, ‘need’, ‘supposed’, ‘would’, and ‘can’ directive expression indicators were found to be most frequent. They were used to mark expectation, indirect question, and they were commonly used to preface requirement, exhortation, and intention. The indicators functioned frequently to facilitate group process (53.45%) and direct learning (42.00%).
3. Analysis of probability expression indicators
There were of 27 types, out of which 9 were frequently occurring. The probability expression indicators were frequently used to mark possibility, prediction, hedging, and logical deductions. The indicators functioned to preface content information given to the students, to mark process facilitation remarks, and to mark facilitators’ questions.
Discussion and conclusion
The study demonstrated the feasibility of using corpus linguistics to study medical students’ knowledge construction talk; provided evidence of knowledge construction through prior knowledge mobilisation, knowledge extension, and enhancement; and signified the attainment of shared knowledge. The facilitators frequently asked lower-order questions; the directive expressions indicators were used to mark content-related and learning behaviour expectations and requirements; and the probability expression indicators were frequently used to mark content information given to the students.
This study shows that students construct knowledge in their PBL tutorials. The pedagogic issues that emerged from the study relates to subversion of the PBL facilitation principles. A wholistic understanding of the factors that affect the behaviours of the facilitators in the classroom is important to resolve this problem. This may involve tutor pedagogic education and recalibration of administrative policies and institutional culture to provide an enabling environment for PBL instructional approach.
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