In this paper, we present a first attempt to integrate Collaboration Engineering with Lean Six Sigma principles applied to Project-based Learning. On the one hand, collaboration is a social and interactive process, where participants join efforts toward a shared common goal. On the other hand, Lean Six Sigma is a unique methodology integrating a shared language, statistical measuring and effective process management techniques, using Axiomatic Design to structure and optimize the training model needed for Project-based Learning. Speaking a common language allows to improve interfunctional problems where people with different competences are involved. Drawing on Collaboration Engineering and the Lean Six Sigma logical model, the paper explores the use of a collaboration ontology to capture and share knowledge about collaboration work and processes.
1.1
Ontology as a tool for knowledge representation has been studied and applied in the
context of many fields such as within information science, knowledge engineering,
artificial intelligence, or digital libraries.
Knowledge engineering approaches have mostly focused on description logics and
formal foundations of ontology design (Gavrilova et al. 2015). However, as Gavrilova
et al. 2015 pointed out, “informal approaches based on human-centered ontology design
processes” have been mostly neglected, despite previous attempts as discussed in, for
example,
Ontologies supporting Information Retrieval based on texts is well understood.
Research in this area comprises mainly semantic search models (
More recent research projects make use of fuzzy concepts in order to reduce what
has been called the semantic gap (Nagarajan et al. 2015; Remi et al. 2015), described
by
An image can be expressed in text by annotating it in relation to the object around it.
Research focuses on text based image retrieval, content based image retrieval, and
ontology based image retrieval techniques
Lastly, ontology alignment and matching techniques, as recently reviewed by
OteroCerdeira et al. (2014), will also play an important part in the proposed project, since
multiple ontologies can be expected to be of relevance to the description of entities in
textbooks as well as for the representation of appropriate ontology facets. Furthermore,
as
Ontology engineering aims at building a formal representation of domain knowledge
(concepts in a domain) and creating a common understanding of the structure of
information in the domain (relations be-tween the concepts) among people or software
agents
Collaboration is very important in many aspects of our lives. When we work together, we can reach goals faster, yield better results and in-spire each other during our collaboration activities. The synergy effects can boost all kinds of endeavors tremendously. However, there are also collaboration efforts that do not work well. Thus, it is very important to be able to assist, analyse and support collaboration with technological means. With this proposal, we want to explore this methodology in a real world example and give best practices on how we suggest to utilize ontologies for the given collaboration process in digital humanities.
In previous work
This section introduces an example of Collaboration Engineering applied to
projectbased learning, using Lean Six Sigma principles, and, specifically, Axiomatic Design
(Suh, 1990), as debated by the authors in
Lean Six Sigma (LSS) is a strategy involving the entire organization in the challenge
to reduce defects to achieve Customer Satisfaction. Therefore, LSS isolates main
criticalities of the whole process in sub-phases, thus dividing the overall problem into
smaller areas to improve process knowledge and to solve it with surgical precise actions
Conventionally, the onset of Project-based Learning (PBL) dates back about one hundred years ago to the work of the educator and philosopher John Dewey, who first implemented the notions of learn by doing into a constructivist pedagogical approach to education. Project-based Learning is learner-centred, involving dynamic classroom approach to stimulate learners’ critical response to solve problems by applying theoretical and technical knowledge, provided by instructors and acquired through previous experiences/study. Significantly, in such hands-on approach aimed at developing learning processes oriented to structured problem-solving education, instructors have their role to evolve to process facilitators, guiding learners’ active exploration of actual (and factual) challenges and problems that they will typically encounter when either applying their learning subjects to real situations. PBL Facilitators challenge learners to develop their technical proficiency and critical thinking skills, as well as teamwork collaborative attitudes and skills to maximize problem solving capability.
As collaborative process, PBL starts from questions to be investigated by providing a full array of hypotheses and explanations. Thus, learners are compelled to critically discuss their ideas face with other learners’ ideas, being encouraged to challenge them and to devise new ones out of ongoing problem-solving debate. The five steps of the process along which PBL usually deploys are effectively met when joined by Continuous Improvement (CI) methodologies and, specifically, Lean Six Sigma, particularly within a framework of interventions driven towards process optimization in a wide range of potential contexts of application. CI and LSS drive learners to a systematic development of their competences (intended as the combination of knowledge, skill, and behaviour), while ensuring deeper knowledge of processes and stimulating towards Operational Excellence.
Based on learn-by-doing approach, LSS training educates the ideal LSS agent according to levels of increasingly complete skills.
The Define-Measure-Analyse-Improve-Control (DMAIC) methodology frames the
entire optimization process, while Axiomatic Design (AD) is the tool to design the LSS
training model, and it is the framework within which to manage the design process,
providing criteria to critically analyse design. Axiomatic Design serves to include all
relevant variables and scenarios, as well as contexts and situations
The engineering design process takes a set of specified inputs and conceptualizes a
design to achieve the desired output effectively along four steps: Problem Definition,
Creative Process, Analytical Process, and Ultimate Check. These same phases are
applicable to map Blooms Taxonomy of cognitive learning
The design process means choosing the right set of DPs to satisfy the given FRs. {FR} = [A] {DP} {FR} = Functional Requirement vector, {DP} = Design Parameter vector, [A] = Design matrix, and the design matrix [A] is of the form: In a good design this mapping should be satisfied by a singular DP to FR relationship or in other words, a particular DP should affect only its referent FR. This because the axiom of independence is violated due to multiple complex relationships, visible through the lack of a square matrix, and off-diagonal relationships.
The multi-level hierarchical (MLH) modelling technique applied by Trewn and Yang
(1998) has been used to determine the dependence of a system reliability
Lean Six Sigma and related process management methodologies could provide the perfect integration with Collaboration Engineering and the Collaboration Ontology that has been developed so far, to create a database for statistically quantified optimization process based on real case scenarios, which can be accessible to users who are not engineers. This would act as an archive for research studies, as a pool for best practice implementation, and could drive studies to enhance LSS methodology as effective optimization strategy. Moreover, this could lead to several applications of LSS-based PBL and to an increase the Collaboration Ontology database, so that all practitioners and scholars involved could “speak the same language”.
Challenges are data collection and data reliability, as well as implementing LSS as shared language. Moreover, since LSS projects are mostly conducted for private customers, ideally such integrated approach could be experimented in the public sector, such as in schools, hospitals, and other public institutions, to optimize processes and promote a shared culture of optimization processes.
All this could be dealt with implementing further participative exchange, with both academia and LSS consultants involved in a continuous exchange of data, building a corpus of shared – and shareable – knowledge and creating a well-structured procedure for project implementation, to ensure the correct qualitative and quantitative research methods to achieve usable data.
In this paper, we presented a first attempt to integrate Collaboration Engineering with Lean Six Sigma principles applied to Project-based Learning.
On the one hand, collaboration is a social and interactive process, where participants join efforts toward a shared common goal.
A Group Support Systems (GSS) can improve the productivity of collaboration work by structuring activities and improving communication.
On the other hand, LSS offers a unique methodology integrating a shared language, statistical measuring and effective process management techniques, using Axiomatic Design to structure and optimize the training model needed for Project-based Learning. Speaking a common language allows to improve interfunctional problems where people with different competences are involved.
Drawing on Collaboration Engineering and the LLS logical model, the paper explored the use of a collaboration ontology to capture and share knowledge about collaboration work and processes.
As upcoming progress of this research, we plan to develop new functionalities for the generic GSS that will reduce the experience needed to design and execute collaboration processes. In this context, we plan to use the collaboration ontology for information retrieval and machine learning approaches. Data and information collected with the generic GSS and the ontology constitute the basis for: • learning recommendation for collaboration tasks; • retrieval of matching collaboration tasks; • Fuzzy Collaboration Task Matching.
In details, re learning recommendations, based on data collected from previous collaboration processes, we want to learn relations combining them with the LSS principles. Then, based on the learned relations, we want to recommend collaboration tasks fulfilling certain conditions.
As for retrieval, similar to the recommendations described above, collected data can be used as the basis for learning relations. However, in contrast to the first scenario, here the goal is to support collaboration engineers.
As regards Fuzzy Matching, the fuzziness of information has been also taken into account. Based on the collaboration ontology, we could introduce a Fuzzy Matching method that can help users in finding interdisciplinary collaboration processes. Llorente, Ainhoa C. (2008): The Use of Ontologies for Improving Image Retrieval and Annotation. Technical Report KMI-08-08, Knowledge Media Institute, The Open University.
Yang, K., Trewn, J. (1998). The relationship between system functions, reliability and dependent failures. In: IEEE international conference on systems man and cybernetics. Institute of electrical engineers inc (IEEE); 1998. p. 4722–7.