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© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_16
16. The COMALAT Approach to Individualized E-Learning in Job-Specific Language Competences
Lefteris Angelis1 , Mahdi Bohlouli2 , Kiki Hatzistavrou1 , George Kakarontzas1 , Julian Lopez3 and Johannes Zenkert2
(1)Aristotle University of Thessaloniki and TEI of Thessaly, Thessaloniki, Larissa, Greece
(2)University of Siegen, Siegen, Germany
(3)University of Alicante, Alicante, Spain
Lefteris Angelis (Corresponding author)
Email: [email protected]
Mahdi Bohlouli
Email: [email protected]
Kiki Hatzistavrou
Email: [email protected]
George Kakarontzas
Email: [email protected]
Julian Lopez
Email: [email protected]
Johannes Zenkert
Email: [email protected]
16.1 Introduction
Language competences are essential in order to find and keep a job and manage everyday life [1]. In [1] it is stated that “Internationalization should become an everyday feature in vocational training. Qualification should include foreign languages and international cooperation between institutions should encourage new approaches to teaching and learning”. So, the challenge is the language proficiency needed for better employability and mobility of people in EU and new, better and more flexible training approaches to support it.
The COMALAT project1 addresses the problem of language hurdles which greatly limits employability and mobility across EU. In this regard, we propose a competence oriented multilingual adaptive language training platform. In our past work we have also used competencies for Human Resources Management in enterprises [2]. This project is important for proper assessment and targeted improvement of the multilingual competencies of youth, adults and (low‑)skilled workers in the European Union (EU) in order to boost their mobility in EU, increase their employability chances and facilitate their cultural adaptation. The current drawbacks of the available language training portals and the previously granted EU projects are: (a) they are not or cannot be considered as OERs (Open Educational Resources), (b) they are not adaptable to the needs and goals of individuals, (c) they cannot be used offline from e. g. disconnected smartphones, (d) they are either targeting the general aspects of the language skills or are very specifically designed for particular groups of people not being flexible in both of these aspects simultaneously, and (e) the software supporting these platforms is not provided with a permissive license and it cannot support a digital marketplace for foreign language e‐learning.
The intellectual outputs of COMALAT have the advantage of being available to all as OER. The platform is competence‐oriented and adaptable to the needs and goals of users. The system will support both online and offline learning, through synchronized apps, on different devices. It is designed so that it can handle different training materials including general language courses as well as specifically designed
courses targeting special interests. We also plan to disseminate the source code of the platform under a commercial‐friendly open source license (e. g. Apache License 2.0). This ensures that anyone can build free and commercial services around the product effectively creating a digital marketplace in language e‐learning.
In the rest of this chapter in Sect. 16.2 we review related works. Then in Sect. 16.3 we provide some technical details of the COMALAT platform. Particularly, in Sect. 16.3.3 we discuss adaptability and statistical evaluation of learners. In Sect. 16.4 we discuss the learning materials for the COMALAT platform. Finally, in Sect. 16.5 we provide some future steps and conclude this chapter.
16.2 Related Work
A large theme of the work in COMALAT is related to adaptability. According to [3] adaptability in e‐learning environments can be related to interaction, course delivery, content discovery and assembly as well as collaboration support. COMALAT will focus its adaptability efforts in adaptive course delivery as well as adaptive collaboration support. Adaptive learning environments require detailed descriptions of learners and learning objects, trying to adapt the learning objects to the needs of the learners. LOM [4] and LIP [5] are two standards that can be used for describing learning objects and learners, respectively. In COMALAT we focus in providing metadata for the learning material, that allow the platform to infer automatically if and what additional content is required for a specific learner. Also learners’ characteristics include, among others, characteristics related to language learning (e. g. desired level can be Beginner or Intermediate), to the reason why someone wants to learn a second language (e. g. the specific purpose) etc.
Descriptions of learning objects and learners are used by appropriate mechanisms that take advantage of this knowledge to adapt the learning activity and facilitate learning [3]. Some approaches that have been proposed towards this direction include [6] in which AI planning is used for course generation but not subsequent adaptation and [7] in which Key Performance Indicators (KPIs) at various levels in business environments as well as ontologies are used for the generation of customized exams as well as learning syllabus. COMALAT’s focus, on the other hand, is course adaptability for language e‐learning.
Also popular e‐learning environments, such as Moodle2 and Sakai3, support all the standard features (learning, collaboration etc.), but lack adaptability features. On the other hand, less known Adaptive Learning Environments, such as Alfanet, Interbook etc., support adaptability features but lack standard e‐learning features [8]. In the COMALAT project a popular platform, supporting all standard features, has been selected and was enriched with adaptability features.
For the recommendation of suitable lessons for learners and the prediction of the effort that is needed in order to reach a specific level, we plan to use in COLAMAT Association Rules Mining (see Sect. 16.3.3 for more details).
Regarding data mining in general in educational environments some indicative works are the following. In [9] the author used web mining techniques to build an agent that could recommend on‐line learning activities and in [10] two technologies were developed in order to construct a personalized learning recommender system; a multi‐attribute evaluation method and a fuzzy matching method to find suitable learning materials. In [11] the researchers focus on the discovery of interesting contrast rules and in identifying attributes characterizing patterns of performance disparity between various groups of students. In [12] evolutionary algorithms as data mining method are used to discover interesting relationships from student’s usage information in order to provide feedback for course content effectiveness. Data mining techniques (association rules and symbolic data analysis) are used in [13] to gain further insight on the students’ learning and improve teaching. The aim of [14] was to guide the search for best fitting transfer model of student learning by using association rules, and the researchers in [15] proposed a framework for personalizing e‐learning based on aggregate usage profiles and a domain ontology. Finally, in [16] Web Usage Mining approach was combined with the basic Association Rules, Apriori Algorithm to optimize the content of an e‐learning portal.
Regarding language materials (Sect. 16.4), there has been some research on the effectiveness of Language Learning Software, Platforms and Apps, in some cases requested by governments making outstanding investments in Language Training. Analysis and assessment of Rosetta Stone™ [17] in 2008 by the Centre of Advanced Study of Language at the University of Maryland outlined some of the main problems (consistently confirmed by research later on [18, 19]) facing independent or self‐access language learning to the present: high levels of attrition, lack of motivation, limited improvement in productive skills, lack of interaction and sense of community, and language learner autonomy issues. Recent related works insist on similar parameters [20], with the addition of the new importance social technologies and media will have in Language Learning environments and, particularly, in learner’s autonomy developments [21].
16.3 The COMALAT Platform
In the frame of the COMALAT project available open source e‐learning software platforms with potential language training capabilities have been investigated. While most of the currently available LMS and CMS are not directly advertised at the market as language training environments, they may provide basic functionality for language e‐learning and assessment. The aim of the e‐learning systems study was to investigate existing features of each platform and compare their suitability for extension towards the language training and assessment platform which is developed in the COMALAT project. We evaluated Moodle, Chamilo, ILIAS, eFront, Sakai, .LRN, Claroline and ATutor. The details of this investigation are beyond the scope of this chapter. However, the final evaluation given several criteria relevant to language learning are given in Table 16.1. Table 16.1Overall platform evaluation
Moodle
Chamilo
ILIAS
eFront
Sakai
.LRN
Claroline
ATutor
Evaluation (in %)
66.85
55.88
55.46
55.73
54.19
50.65
53.07
54.10
This evaluation was only supportive for the purpose of the COMALAT project, and finally Sakai was selected. First, there was a constraint that the platform selected should have a permissive Open‐Source License which would allow its commercial use. This indicated Sakai as a possible candidate. Nevertheless, we wanted to know the advantages and disadvantages of Sakai in relation to other platforms and this is why the detailed evaluation and comparison was carried out. Furthermore, Sakai was quite high in the list in our comparison of LMS’s, although not the first choice, without any serious shortcomings for language e‐learning. At a technical level Sakai’s functionality is provided by mostly independent tools. Tools use the Sakai framework which provides common functionality shared by all tools. Examples of tools are the Grade Book tool, the Syllabus tool, the Samigo (aka Tests and Quizzes) tool and many others. For the purposes of the COMALAT project we have chosen to provide two additional tools which are shortly described next.
16.3.1 COMALAT Guide Tool
This tool is responsible for providing guidance during users’ interaction with the system. It provides a learning path comprising of lessons and quizzes. Based on results, the tool provides additional content to learners according to their needs. This is one aspect of the adaptability of the learning content to the users. In addition, users get instructions in their chosen instruction language (English, Spanish, German) and specify their job‐specific preference (Health, Tourism & Hospitality, Science & Technology and Business & Professional Language). This information is provided in the user’s profile using the Guide tool profi
le page (see Fig. 16.1).
Fig. 16.1COMALAT Guide profile page
The provided guiding functionality by this tool, makes it possible to combine and include other tools, like the Test & Quizzes tool. As a result, sequences of learning materials, like the implementation of a learning path for language training can be realized inside Sakai. Technically, the Guide tool uses the “Group” concept of Sakai to filter the suitable content for users. Users are registered to and are unregistered from specific groups depending on their achievements in specific activities. As a final result, the content is made visible to the learner at the right time and is delivered step‐by‐step using the Guide tool.
16.3.2 COMALAT Authoring Tool
This tool is used by the teachers who upload content to the system. It allows them to provide specific metadata for this content, as can be seen in Fig. 16.2.
Fig. 16.2COMALAT Authoring Tool
Provided metadata can be in relation to isolated activities spread throughout the lessons in a learning path as well as metadata for final tests. Furthermore, the system is extensible in the sense that it allows teachers to define further metadata tags than the ones already defined. For example, an activity within a lesson can be characterized by the path as normal or extra, with extra activities being presented to learners as additional content when they fail to complete successfully the normally provided activities.
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