Towards A Digital Repository Of Ship-operations

Towards A Digital Repository of Ship-Operations: The Capture and Management of a Knowledge-base for Safety & Education Kalyan Chatterjea1, Rose Alinda2, Pradeep Chandrasen Nadkar3 1 Head, Research & Consultancy, Malaysian Maritime Academy, Malacca, Malaysia [[email protected]] 2 Professor Computer Science, Dean, Graduate School (Engineering), University Technology Malaysia [[email protected]] 3 Director, Training & Education, Malaysian Maritime Academy, Malacca, Malaysia [[email protected]]

Abstract: While the essential knowledge domain for ship-operation is large and growing rapidly, and the available time for the proper training of maritime professionals is perhaps shrinking to meet the growing industry demand, it is becoming more & more essential to ensure proper capture and management of this important knowledgebase. Poor management of this knowledge area may not only result in gaps in training but breaches of safety in critical shipboard procedures and perhaps further aggravated by the attrition of trained shipboard personnel as they move to work ashore. In the paper, the authors will address these issues and describe a possible dynamic digital knowledge-capture strategy, and the development of an incrementally growing maritime digital knowledge repository, which could not only alleviate some of these problems but lead to an overall improvement in ship safety and operations. The authors will also share the methodology, presently being planned at the Malaysian Maritime Academy (MMA), and which is likely to lead to avenues of collaborative work between MMA, academia (UTM) and shipping companies. [Key Words: Knowledge-capture, digital repository, knowledge-base, concept map, collaborative ontology, student-centered learning, work-integrated learning, information visualization, knowledge visualization, domain ontology and task ontology.]

1. Introduction Shipboard technology and work procedures are changing rapidly just as the work practices ashore. Managing teaching and learning resources at Maritime Education and Training (MET) Institutes are becoming more challenging. In previous decades, MET lecturers could fall back on their own shipboard experiences. Today they find themselves lacking in the areas of procedural knowledge and also on knowledge of the latest equipment on board. The books, which used to be good sources of information are dated and cannot provide the required knowledge sought by ships’ operators. While referring to traditional training in MET Institutes, Dr Barry Strauch of US National Transportation Safety Board, said In the US Merchant Marine Academy (USMMA) Seminar on Bridge Resource Management (Strauch, 2008) “Traditional training primarily: • Ensures familiarity • Overlooks proficiency • Overlooks most challenges presented by highly automated systems”

Page 1 of 11

At the same conference, Captain George Sandberg, Professor and Director of Nautical Science Simulation at the US Merchant Marine Academy questioned the efficacies of introducing advanced navigational equipment on the bridge without relevant training. "Advanced navigation systems are being fitted and used aboard ships at a rate faster than best practices are established. Many ships today are using ECDIS routinely, but the best practice for the use of that equipment has not really been established. Do the majority of today's mariners know how to use this new equipment effectively or correctly? And, of even more concern, due to self-teaching, do they really understand the capabilities of the equipment, or do they have misunderstandings of how to use it?"

Recognizing that in today’s shipboard practice, procedural changes are very dynamic, it becomes evident that MET institutions require constant updating and revision of knowledge delivery using technology in order to keep pace and remain relevant in the fast changing maritime industry. For such a pursuit, the stakeholders, namely, the MET Institutions, the Shipping Companies, Academia (specializing in technology for knowledge management) and most importantly, the Learners should be involved in a collaborative agreement to synergize the project processes. In this paper, we share some of our plans at the Malaysian Maritime Academy for acquiring this important knowledge, as it is practiced on board. This is perceived to be made up of the knowledge about the (1) equipment incorporating new technology as well as the (2) best practices for safe operation of systems incorporating such technology. The paper would attempt to describe the proposed knowledge capture methodologies and the depiction of the captured knowledgebase for • quick accessing • use in teaching, learning and • dynamic updating

2. Strategy for Knowledge-capture 2.1 Knowledge-capture Arena

“...knowledge and learning occur within a domain of practice, within a world view, within a context. The process of acquiring knowledge is not like a transfer, as though knowledge were a set of blocks, but a process of immersion, as through knowledge were a body of water.” [Downes, 2004]

Serova (2009) revealed findings at Intel, when they studied contributions of various learning sources towards actual productivity at the workplace. Fig. 1 shows the findings, which attributes only 10% to formal institutionalized learning, 20% to networking and the major learning through one’s own experience on the job. Other studies also endorse learning-to-

Page 2 of 11

knowing transformation in the “ambient community of practice” (Lave & Wenger, 1991; Chia-an and Yin, 2003). Hence, capturing through work-integrated leaning appears lucrative, especially in maritime, where learners frequently revisit MET institutions to fulfill their STCW requirements. This allows for a shipboard knowledge-capture arena (see Figure 1.), which could function as a knowledge-lab for learners. Given the right assignments and adequate guidance, the learner could help populate and update the institutional knowledgebase with regular update cycles.

Shipboard Work-integrated Learning & Knowledge-capture

Figure 1 Planned Knowledge-capture Arena is based on the findings of Intel Research (Source: Serova, 2009)

2.1

Knowledge-capture Methodology “...content – whether in the form of textbooks or in the form of learning objects – can never be at the centre of a learning environment, but may only form a part of the surround. They way people learn, quite literally, is to swim in knowledge, and the way to swim, is to have a direction to swim to. Learning communities provide that direction, and a new learner is, as it were, swept up with the school as it swims in a certain way, in a certain direction.” [Downes, 2004]

Referring to knowledge-capture, Stephen Downes (ibid.) says that it “consists of being able to capture those artifacts, of being able to recognize the salient features of the context of transmission, of being able to store them in digital form, and of being able to recognize new contexts in which the same transmission would be of value. Though this sounds daunting, it Page 3 of 11

has already been accomplished in simple form; the trick is to make it work with the complexity and richness of actual human communication.” To simplify the shipboard experience transfer to the MET Institute, we plan to build a domain ontology, which is less formal (not directly machine-readable), meaning, having more human-like semantics. An ontology is an explicit specification of a conceptualization (Gruber, 1993). In a community driven ontology building, complexities of formal development can create barriers to the development process. While, in a less formal process, there is faster progression and the number of participants can be scaled up (Hepp et al., 2006; de Moor et al., 2006). Hence, such a system can lend itself towards higher reuse. To develop a community driven ontology, we plan to use concept maps. Concept maps are frequently used for knowledge elicitation and visual depiction of knowledge (Castro et al. 2006; Allert et al. 2006; Van Damme et al. 2007). Concept maps describe a domain using simple graphical representation of concepts, which are shown as nodes and the relationships between these concepts are sown as arcs. A unit of knowledge is sometimes referred to as a triplet (or a proposition) and consists of at least two concepts (or nodes) and a relationship (an arc with some meaningful phrase). Propositions contain two or more concepts connected using linking words or phrases to form a meaningful statement (Novak & Cañas, 2008). To develop more complex relationships more triplets could be added and to represent a large domain, many such maps could be integrated. Additionally, the nodes and relationships can be provided with topical resources to enhance richness of the knowledge representation (Chatterjea and Nakazawa, 2007). Details of community driven maritime ontology development (Chatterjea, 2006; Chatterjea and Nakazawa, 2008) was reported earlier and a longitudinal study could now be undertaken to exploit the potential of this platform. The next sections elaborate the strengths of information and knowledge visualizations, which will be an integral part of the ontology development. 2.2 Information and Knowledge visualization “Information visualization has been a research topic for many years, leading to a mature field where guidelines and practices are well established. Knowledge visualization, in contrast, is a relatively new area of research that has received more attention recently due to the interest from the business community in Knowledge Management.”[ Cañas et al. 2005]

Burkhard and Meier (2004) defined knowledge visualization as the use of visual representations to transfer knowledge between at least two persons. Burkhard (2005) also differentiated information and knowledge visualization, which is depicted in Figure 2.

Page 4 of 11

Figure 2 Information and knowledge visualization: Burkhard’s ( 2005) differentiation with modification

Burkhard and Meier (2004) further developed a Knowledge Visualization Framework consisting of four perspectives that need to be considered when creating visual representations that aim to transfer and create knowledge. The framework is shown in Figure 2.

Figure 3 Knowledge Visualization Framework (Modified from: Burkhard and Meier, 2004)

Page 5 of 11

An example of knowledge visualization is shown in Fig. 4 below. It provides an advanced user interface for training support, where the deconstruction of a major task of activating an LNG tanker from the dry dock to full-away conditions using such an interface is shown. The nodes in the map are embedded with learning resources. The numbers in figure (1), (2)..(20) refer to the simulation exercises. It is expected that as the learners go through these sequential tasks on the steam-propulsion simulator, they would access the resources and thereby pick up the essential knowledge and proficiency required for the task at-hand. They will also be able to assess themselves using the built-in e-assessment components, embedded in this advanced learning organiser. Localisation of knowledge using concept mapping tools can provide resources for self-regulated resource-based learning (Tergan and Haller, 2003).

Figure 4 Example of Knowledge Visualization: Advanced Knowledge Organiser for use in Simulation-based Learning (Source: IMO STCW Revision Document STW 39/7/19 – 2007)

Concept mapping tools support situational relevance on spatial representations, which could breakdown complex learning tasks into manageable learning objects, with their own resources. Graphical representations in concept maps enhance cognitive process of managing knowledge and information in resource-based learning and problem solving environments. 2.3 Domain and Task Ontology Ontological analysis clarifies the structure of knowledge. The first reason is that they form the heart of any system of knowledge representation. If we do not have the conceptualizations that underlie knowledge, then we do not have a vocabulary for representing knowledge. Thus the first step in knowledge representation is performing an effective ontological analysis of some field of knowledge. (Chandrasekaran et al., 1998)

A domain ontology is a formalisation of the knowledge in a subject area (domain) such

Page 6 of 11

as stability, navigation, thermodynamics etc. This could be considered as static knowledge from a particular perspective and is frequently referred to as declarative and classificatory (Ferguson-Hessler and de Jong, 1990; Cañas and Novak, 2006). It is usually arranged hierarchically (see example in Figure 5). Domain ontology differs from other types of ontology such as the task ontology (a formalisation of the knowledge necessary to solve a specific problem or task e.g. warming up the propulsion engine, changing over fuel or making fast of the vessel after arriving port). This type of knowledge is procedural, could be evolving or considered to be dynamic in nature (see example in Figure. 4) and is referred to as cyclic or sequential knowledge (Safayeni, et al., 2005).

Figure 5 Declarative or classificatory knowledge: domain ontology of steam turbines /boiler protection

The differentiation, described above, conforms to maritime practice, where we differentiate between knowledge (e.g. knowledge of thermodynamics or stability) and skill (e.g. skill/ proficiency of operating a steam boiler or interpreting ship’s radar-plotting). Hence, we could organized the knowledgebase into two distinct areas and call them (1) knowledge, Page 7 of 11

which is meant to be classificatory, defining the basic concepts of the domain and (2) proficiency which is explanatory, more dynamic in nature and could be procedural tacit knowledge of experts. The classificatory knowledge could be referred to as being objectbased and proficiency to be event-based (Cañas and Novak, 2006).

Figure 6 Steps and Milestones suggested by Castro et al. (2006)

Steps and milestones for the project for developing the concept-map-based knowledge are to follow the guideline given by Castro et al. (2006). The main challenge for the project would be to develop the items 1, 2 and 3. Ontology refinement and formalisation efforts will be undertaken at a much later stage as the tools for these processes are still evolving (see details at http://coe.ihmc.us/groups/coe/). So, when these tools are matured, we could progress with the remaining stages, which will make the searching processes much more efficient. 2.4 Knowledge capture at sea Perhaps, the easiest channel of capturing updated operational knowledge from the practitioners is when our engineers/officers go to sea for their sea-time and get exposed to both domain ontology as well as the procedural knowledge as practiced on board. Appropriate assignments should be given to the engineers and officers to capture these ontologies. The process will ensure currency of both types of the ontologies discussed earlier and as the engineers/ officers are placed on various ships, the different case-studies captured will enrich the knowledge-base with adequate diversity. Thus, the trainee officers/engineers would be asked to develop their portfolios of organised knowledge,

Page 8 of 11

which could be vetted by seniors on the ships and by the facilitators at the MET Institute, before these are allowed to populate a digital repository at the Institute.

3. A Digital Repository Visual ontologies with resources attached to nodes lend themselves as efficient knowledge organisers and are reported to be suitable for digital libraries or repositories with text-based searching facility. Concept maps created by learners serve as summarization of a domain. In a digital repository learning environment, it is claimed, that concept maps could further engage both learners and instructors in an active learning approach and collaboration setting (Shen et al., 2006; Kumar and Schwertner, 2008).

4. Conclusion The paper described a large-scale knowledge-based project plan to improve teaching and learning undertaken at the Malaysian Maritime Academy. The experience gained in smaller pilot projects earlier; provide the impetus for this project. The project has the potential to reduce the knowledge and proficiency gaps for the mariners of the future and thereby improve operational safety and reduce human error. For successful implementation, the project will need to get the support of industry stakeholders as well as technology support from the academia.

References Allert, H., Markkanen, H, and Richter, C.(2006). Rethinking the Use of Ontologies in Learning. E. Tomadaki and P. Scott (Eds.): Innovative Approaches for Learning and Knowledge Sharing. EC-TEL 2006 Workshops Proceedings, ISSN 1613-0073, p. 115-125. Retrieved on 2nd September 2009 from: http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-213/paper21.pdf Burkhard, R., & Meier, M. (2004).Tube Map Visualization: Evaluation of a Novel Knowledge Visualization Application for the Transfer of Knowledge in Long-Term Projects. Journal of Universal Computer Science, vol. 11, no. 4 (2005), 473-494. Retrieved on 2nd September 2009 from: http://www.jucs.org/jucs_11_4/tube_map_visualization_evaluation/Burkhard_R_A.pdf Burkhard, R., (2005). "Towards a Framework and a Model for Knowledge Visualization: Synergies between Information and Knowledge Visualization", in: Tergan and Keller (eds.) Knowledge and information visualization: searching for synergies, Heidelberg / New York, Springer Lecture Notes in Computer Science, [to appear]. Cañas, A., Carff, R., Hill, G., Carvalho,M., Arguedas,M., Eskridge,T.C., Lott, J. and Carvajal, R.(2005). Maps: Integrating Knowledge and Information Visualizationin. In S.-O. Tergan, & T. Keller, (Eds.) Knowledge and information visualization: Searching for synergies. Heidelberg / New York: Springer Lecture Notes in Computer Science.

Page 9 of 11

Cañas, A. J. & Novak, J. D. (2006). Re-Examining The Foundations For Effective Use Of Concept Maps. In A. J. Cañas & J. D. Novak (Eds.), Concept Maps: Theory, Methodology, Technology. Proceedings of the 2nd International Conference on Concept Mapping. San José, Costa Rica. Castro, A.G., Rocca-Serra, P., Stevens, R., Taylor, C., Nashar, K., Ragan, M.A. and Sansone,S.(2006). The use of concept maps during knowledge elicitation in ontology development processes – the nutrigenomics use case. Published online 2006 May 25.Copyright © 2006 Castro et al; licensee BioMed Central Ltd. Retrieved on 2nd September 2009 from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1524992 Chandrasekaran, B., Josephson, J.R. and Benjamins, R. The Ontology of Tasks and Methods, In Proceedings of the 11th Knowledge Acquisition Modeling and Management Workshop, KAW'98, Banff, Canada, April 1998. Retrieved on 7th September 2009 from: http://www.cse.ohio-state.edu/~chandra/Ontology-of-Tasks-Methods.PDF Chatterjea, K. and Nakazawa, T. (2008). Modeling A Knowledge Domain For A Maritime Course. Proceedings of the 3rd International Conference on Concept Mapping CMC2008, Tallinn, Estonia/ Helsinki, Finland. 22-25 Sept 2008. Chatterjea, K. (2005). Changing Classrooms into Knowledge Laboratories – A Possible Scenario Replacing Everyday Lectures. Singapore Polytechnic Journal of Teaching Practice, 2006. Retrieved on 2nd September 2009 from: http://www.pdfcoke.com/doc/1902964/Changing-Classrooms-Into-Knowledge-Laboratories Chatterjea, K. and Nakazawa, T. (2007). Development of Resource-based Learning Organisers to Enhance Training on Simulators. Paper Presented at the 8th International Conference on Engine Room Simulators (ICERS 8) Held in Manila, Philippines, 5-8 November 2007. Retrieved on 2nd September 2009 from: http://www.pdfcoke.com/doc/1888779/Development-of-Resourcebased-Learning-Organisers-to-Enhance-Training-on-Simulators

Chia-an and Yin, Roger Li-chung, (2003). From Workplace Learning to Knowledge Management: Implications For Organizational Advancement, Proceedings for Refereed Presentation 22nd Annual Research Conference, Organizational Systems Research Association. Retrieved on 2nd September 2009 from: http://www.osra.org/2003/chao.pdf de Moor A., et al., 2006. DOGMA-MESS: A Meaning Evolution Support System for Interorganizational Ontology Engineering. Proceedings of the 14th International Conference on Conceptual Structures (ICCS 2006). Aalborg, Denmark, pp. 189-203. Downes, S. (2004). Creating and Capturing New Knowledge, Australian Flexible Learning Community (2001-2004). Retrieved on 2nd September 2009 from: http://community.flexiblelearning.net.au/GlobalPerspectives/content/article_5311.htm Fergurson-Hessler, M.G.M., & de Jong, T. (1990). Studying physics texts: differences in study processes between good and poor performers. Cognition and Instruction. 7, 41-54. Gruber Thomas R. (1993). A Translation Approach to Portable Ontology Specifications. In Knowledge Acquisition, Vol. 5, No. 2, pp.199–220.

Page 10 of 11

Hayes, P. at al., (2005). Collaborative knowledge capture in ontologies. Proceedings of the 3rd international conference on Knowledge capture. New York, USA, pp. 99–106. IMO STCW Revision Document STW 39/7/19 – (2007), submitted by Singapore. Comprehensive Review of The STCW Convention and The STCW Code - 39th session Agenda item 7. Retrieved on 7th September 2009 from: http://www.sjofartsverket.se/pages/13770/39-7-19.pdf Kő, A., Biró,M., Gábor, A., Vas R.(2004).Knowledge Representation and Sharing for Identity Management Considering European Values. eChallenges 2004 Conference. Vienna. October 27-29, 2004. Retrieved on 2nd September 2009 from: http://www.google.com/url?sa=t&source=web&ct=res&cd=1&url=http%3A%2F%2Fistrg.som.surrey.ac.uk%2Fprojects%2Fguide%2Ffiles%2FBUESPA-GUIDEEChallenges-jav4.doc&ei=UTqeSqTuFZiTkQWjgsHZBA&usg=AFQjCNGbqaMFJrFV1KtOBiCHFBGShqM8YQ&sig2=BtIwM2BpJnAXdvjVZJ6GuQ

Kumar, A. and Schwertner, N. (2008) Building Personal Collections and Networks of Digital Objects in a Fedora Repository Using VUE. In: Third International Conference on Open Repositories 2008, 1-4 April 2008, Southampton, United Kingdom. Novak, J. D. & A. J. Cañas. (2008) The Theory Underlying Concept Maps and How to Construct and Use Them, Technical Report IHMC CmapTools 2006-01 Rev 01-2008, Florida Institute for Human and Machine Cognition, 2008. Retrieved on 2nd September 2009 from: http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf Saad, A and Zaghloul,A.-R.M. (2002).A Knowledge Visualization Tool For Teaching And Learning Computer Engineering Knowledge, Concepts, and Skills. 32nd ASEE/IEEE Frontiers in Education Conference. T2F-7. November 6 - 9, 2002, Boston, MA Safayeni, F., Derbentseva, N., & Cañas, A. J. (2005). A Theoretical Note on Concept Maps and the Need for Cyclic Concept Maps. Journal of Research in Science Teaching, 42(7), 741-766. Serova A, (2009). Learning 2.0 in action: Intel business case, Asia eLearning Conference 2009. Kuala Lampur. Shen R, Richardson R, and Fox E.A.(2006). Concept Maps as Visual Interfaces to Digital Libraries: Summarization, Collaboration, and Automatic Generation. Retrieved on 7th September 2009 from: http://vw.indiana.edu/ivira03/shen-et-al.pdf Strauch, B. (2008). Technology and the training challenge. Digital Ship October 2008. Pp.31-33. Retrieved on 2nd September 2009 from: http://www.pdfcoke.com/doc/6959848/DIGITAL-SHIP-OCT-2008 Tergan, S.-O. & Haller, H. (2003). Organization, representation, and localization of knowledge with mapping tools. Paper presented at the 10th Biennial Conference of the European Association for Research on Learning and Instruction. University of Padova, Padova / Italy (August 26-30, 2003). Retrieved on 7th September 2009 from: http://heikohaller.de/literatur/Tergan_Haller_2003.pdf Van Damme, C. , Christiaens, S., Vandijck, E. (2007). Building an employee-driven CRM ontology. Proceedings of the IADIS Multi Conference on Computer Science and Information Systems (MCCSIS): E-society2007, Lisbon, Portugal, pp.330-334. Retrieved on 2nd September 2009 from: http://celinevandamme.com/VanDammeChristiaensVandijck_MCCSIS2007_draft.pdf

Page 11 of 11