April 2014 Theme: Semantics in Education
Guest Editors' Introduction: Fabrizio Lamberti, Valentina Gatteschi, Claudio Demartini, Andrea Sanna, and Paolo Montuschi

Semantics represents the backbone of several of today’s technological trends and visions, from the Internet of Things (IoT) and Web of Things (WoT) paradigms to the Open Data and Linked Data initiatives. Moreover, it is increasingly advertised as a key enabling technology in a growing number of application domains. In the past ten years, we at Polytechnic University of Turin have been considering the possible benefits of applying semantic technologies to the field of education. For instance, in two recently closed research initiatives, cofunded by the European Commission under the Lifelong Learning Programme, we studied how to exploit semantics to find common or missing learning units across transnational qualifications (TAMTAM project) and to fill the competency gap between learners and job-seekers (MATCH project).

But … What is Semantics?

The term semantics (from the Greek sēmantikós, which means “important”) traditionally refers to the study of the meaning of words, symbols, signs, and so on. Hence, it has long been associated with humanistic disciplines such as philosophy, philology, communication, and semiotics.

In recent years, semantics has also begun to play a central role in more technical fields — particularly in the Internet domain, and often in combination with the expression “Semantic Web.” According to Tim Berners-Lee’s vision of Web 2.0, semantics is the core technology required to enable the envisioned shift from a “Web of documents” to a “Web of data.” In such a revolutionary scenario, traditional Web pages would largely be replaced by heterogeneous information repositories that could be transparently accessed by both humans and machines.

This vision and efforts by organizations such as the World Wide Web Consortium (W3C) have profoundly transformed the role of Web end users over the past decade. Mere (data) consumers have started to actively take part in producing new knowledge, as witnessed by the growing spread of communication tools such as wikis, blogs, and social networks. Following a similar trend, machine-produced content is progressively being organized in a structured way that can be shared and linked to any related information.

Such efforts have created an extraordinary amount of digital content that continues to grow exponentially. In fact, analysts estimate that more than 2.5 quintillion (that’s 2.5×1018) bytes of data are generated every day.

This outlines a challenge that’s of paramount importance in the new scenario created by the Web 2.0 revolution — that is, the complexity associated with effectively exploiting the (new) knowledge available yet enabling the production of tangible added value in all relevant application fields.

According to the vision of semantics and the Semantic Web, the “recipe” for handling such challenges is to keep data separate from presentation, and to annotate it with metadata. Enriching informative content with embedded information about its meaning can allow humans and computers to exploit existing knowledge in incredible new ways.

Not surprisingly, earlier applications of semantic technologies were aimed at solving knowledge-intensive problems, mainly in the fields of bioinformatics and health sciences. From there, researchers exploited semantics for defining data-exchange and integration formats, as well as for implementing distributed solutions for e-commerce, home automation, supply-chain management, and many other application fields.

In today’s Web, in which manual data processing has become almost impossible, we look at semantics primarily when we need “intelligent” methods for knowledge management that go beyond basic literal-lexical elaborations — as when searching, filtering, matching, aggregating, and recommending resources in a way that mimics human reasoning.

Opportunities for Semantic Processing in Education

Given the increasing volume of learning and teaching resources available in online repositories, the growing number of learners choosing Web-based environments for studying, and the need to link heterogeneous data to implement the spreading vision of lifelong learning, the possibilities offered by semantic technologies are increasingly relevant in the world of education.

Thus, for instance, an open call for papers for the upcoming IEEE Transactions on Emerging Topics in Computing special issue on Advances in Semantic Computing (pdf) lists education among the key application fields of interest. Similarly, IEEE Transactions on Leaning Technologies published a special section on Semantic Technologies for Learning and Teaching Support in Higher Educationin 2012. For an overview of the main themes tackled by the articles in that special section, see the following video by one of the guest editors, Thanassis Tiropanis.

Although researchers have acknowledged the potential benefits of using semantic technologies in educational contexts since early experiments, the efforts required to make the knowledge in the resources machine-understandable hindered initial adoption.

Any semantic system requires the framing of all concepts of interest for a given domain into formal structured models, including representations of the relations among the concepts. From there, the modeled concepts are used in metadata to annotate resources.

Achieving the required consensus is challenging when building holistic models, such as taxonomies or ontologies, to represent the multiplicity of concepts in a given domain. Involving all the relevant stakeholders (and reaching agreement on the model) is generally difficult, regardless of whether prior classifications exist (in which case, they might be deeply rooted in a particular culture, sector, or region). Annotation presents comparable complexity because domain experts such as teachers or qualification designers are generally tasked with manually adding metadata — often with little or no knowledge of semantics.

As communities began developing models that were then reused in many different fields for formalizing specific domain knowledge, as well as proposing lightweight solutions for annotation (based on social or semi-automatic tagging, for example), the conditions became suitable for the practical application of semantics to education.

In this month’s Computing Now theme, we present a sketch of some of the most current trends in the field, as seen from the perspective of the various actors, including learners, educators, education organizations, workers, and employers. In particular, we showcase how semantic technologies could:

  • Support the creation, delivery, and revision of qualifications, courses, and related learning materials — for instance, comparing existing courses to remove overlaps or designing new courses by identifying deficiencies at other institutions or addressing needs from the labor world;
  • Create computer-based tutoring systems that could assist students by recommending suitable subjects to fill their competency gaps — creating personalized paths and identifying the educators that could best support them;
  • Simplify and improve access to learning objects within and across institutions by supporting the creation of wide interoperable knowledge repositories and using context cues (about the particular field of study, type of learning activity, and so on) to effectively match content to queries by students and teachers;
  • Foster collaborative learning and critical thinking approaches by making it easier to create environments that let users share experiences, and by simplifying the process of setting up groups based on students’ backgrounds, preferences, and so on;
  • Support placement activities — in the context of traineeships or internships, for example, as well as in sorting out the best alternatives in job placement scenarios, aligning learners’ (and workers’) resumes and employers’ offers; and
  • Support strategies aimed at improving transparency and readability in educational processes and outcomes, thus easing the implementation of policies linked to quality assurance, accreditation, certification, referencing national or international regulating frameworks, and so forth.

Theme Articles

For the first article in this month’s theme, we selected the must-read reference work “Semantic Technologies for Learning and Teaching in the Web 2.0 Era.” In this article, Thanassis Tiropanis and his colleagues report on a survey that analyzed higher-education institutions’ use of semantics in the United Kingdom. In addition to categorizing the relevant challenges, the authors classify the various tools that have been adopted for operating on semantic-based resources and document the degree of adoption, drafting areas that need further research.

In “A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning,” Mihaela M. Brut, Florence Sedes, and Stefan Daniel Dumitrescu, exemplify the efforts needed to cope with any semantic system’s implementation requirements. Focusing on the world of e-learning, the authors show how to extend the well-known IEEE learning object metadata (LOM) standard with ontology-based metadata and to automatically annotate learning resources by means of latent semantic indexing and thesaurus-based text processing.

The three other articles in this month’s theme form an intriguing overview of application scenarios in which researchers and developers have recently experimented with semantics, including computer-based assessment and tutoring, smart learning environments, and optimal course and degree design.

Claus Pahl and Claire Kenny’s “Interactive Correction and Recommendation for Computer Language Learning and Training” presents an automated learning and skills-training system in the field of database programming. The system exploits a semantic-based error classification and correction engine to provide students with feedback on the correctness of exercise solutions as well as with personalized guidance by recommending further study materials.

In “Adaptive System for Collaborative Online Laboratories,” Christopher Gravier and his colleagues analyze online laboratories using a group awareness layer that replicates the collaborative aspects of learning in local laboratories. In their system, an ontology-based intelligent component manages semantic-based policies designed to let users effectively operate on remotely shared resources.

In the last article, “Foundation for Modeling University Curricula in Terms of Multiple Learning Goal Sets,” Richard Gluga, Judy Kay, and Tim Lever study how to apply semantics to degree-program design. They leverage semantic technologies to help ensure that the sequence of learning activities, topics, and assessments creates an effective progression over the course of the degree program.

These theme articles highlight just some of the current research directions in the field. Readers interested in further insights on other relevant application areas for semantics in education should consider the Additional Resources sidebar, which points to other articles from the IEEE Computer Society Digital Library.

Industry Perspectives

This month, we also have two Industry Perspective videos. The first is from Ed Mahood, strategic projects expert at DEKRA Akademie in Germany, who describes how semantic technologies can address vocational and professional education providers’ qualification and requalification needs. In the second video, Terry Hook, managing director of clock-IT-skills in the United Kingdom, uses his experience in developing the European e-Competence Framework to illustrate how semantics could help in the development of standards of learning outcomes.


We also feature a set of videos this month highlighting perspectives from respected academics and researchers. Director of the MERLOT.org program at California State University, Fullerton, and President Emeritus of the IEEE Computer Society, Sorel Reisman begins the discussion by highlighting semantic technologies’ relevance to technology-enabled learning by pointing out how they could enable a shift from present e-teaching approaches to true individualized/adaptive e-learning solutions. From there, Jean-Luc Gaudiot of the University of California, Irvine, Enrico Bressan of Centro Produttività Veneto, Italy, and Andrea Covini of the Italian Association for Computing look into the challenges, opportunities, and future trends of semantics- and technology-based education. Covini additionally raises attention to the AICA’s (http://www.aica.it) DIDAMATICA 2014 conference (http://didamatica2014.unina.it) on teaching and education.




The opportunities for semantic technologies in education are enormous. They offer a mechanism to address technical, organizational, linguistic, and other constraints that can hinder the seamless sharing, processing, and adaptation of heterogeneous learning resources. We invite you to dig into the wealth of possibilities, starting with the topics in this month’s theme.

Additional Resources
on Semantics in Education

The following articles can offer further insights about semantics in education.

  • D. Gasevic et al., “An Approach to Folksonomy-Based Ontology Maintenance for Learning Environments,” IEEE Transactions on Learning Technologies, vol. 4, no. 4, 2011, pp. 301–314.
  • K. Scott and R. Benlamri, “Context-Aware Services for Smart Learning Spaces,” IEEE Transactions on Learning Technologies, vol. 3, no. 3, 2010, pp. 214–227.
  • E.F. Risko et al., “The Collaborative Lecture Annotation Sytem (CLAS): A New TOOL for Distributed Learning,” IEEE Transactions on Learning Technologies, vol. 6, no. 1, 2013, pp. 4–13.
  • S.K. D’Mello and A. Graesser, “Language and Discourse are Powerful Signals of Student Emotions During Tutoring,” IEEE Transactions on Learning Technologies, vol. 5, no. 4, 2012, pp. 304–317.
  • M. Hatala et al., “Ontology Extraction Tools: An Empirical Study with Educators,” IEEE Transactions on Learning Technologies, vol. 5, no. 3, 2012, pp. 275–289.
  • L. Cutrone and M. Chang, “Automarking: Automatic Assessment of Open Questions,” Proc. 10th IEEE Int’l Conf. on Advanced Learning Technologies, 2010, pp. 143–147.
  • E. Snow, C. Moghrabi, P. Fournier-Viger, “Assessing Procedural Knowledge in Free-text Answers through a Hybrid Semantic Web Approach,” Proc. 25th IEEE International Conference on Tools with Artificial Intelligence, 2013, pp. 698–706.
  • D. Celik, A. Elci, E. Elverici, “Finding Suitable Course Material through a Semantic Search Agent for Learning Management Systems of Distance Education,” Proc. 35th IEEE Annual Computer Software and Applications Conference Workshops, 2011, pp. 386–391.
  • P. Montuschi, V. Gatteschi, F. Lamberti, A. Sanna, C. Demartini, “Job recruitment and job seeking processes: how technology can help”, IT Professional, In Press.
  • Garrido, E. Onaindia, “Assembling Learning Objects for Personalized Learning: An AI Planning Perspective,” IEEE Intelligent Systems, 2013, pp. 64–73.
  • V. Dimitrova, “Semantic Social Scaffolding for Capturing and Sharing Dissertation Experience,” IEEE Transactions on Learning Technologies, vol. 4, no. 1, 2011, pp. 74–87.
  • Magnisalis, S. Demetriadis, A. Karakostas, “Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field,” IEEE Transactions on Learning Technologies, vol. 4, no. 1, 2011, pp. 5–20.
  • M. Liu et al., “Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support,” IEEE Transactions on Learning Technologies, vol. 5, no. 3, 2012, pp. 251–263.
  • K. Verbert et al., “Context-Aware Recommender Systems for Learning: A Survey and Future Challenges,” IEEE Transactions on Learning Technologies, vol. 5, no. 4, 2012, pp. 318–335.


Fabrizio Lamberti et al., “Semantics in Education,” Computing Now, vol. 7, no. 4, Apr. 2014, IEEE Computer Society [online]; http://www.computer.org/publications/tech-news/computing-now/semantics-in-education.

Fabrizio Lamberti is an assistant professor at the Polytechnic University of Turin, Italy. His research interests include computational intelligence, semantic processing, distributed computing, human-computer interaction, computer graphics, and visualization. Lamberti is a Senior Member of IEEE and the IEEE Computer Society. He has published more than 90 papers in international peer-reviewed journals, magazines, and conference proceedings. Contact him at fabrizio.lamberti@polito.it.

Valentina Gatteschi – Valentina Gatteschi is a postdoctoral research assistant at the Polytechnic University of Turin, Italy. Her main research interests are in semantics and natural language processing. Gatteschi has been involved in several European projects on education. Contact her at valentina.gatteschi@polito.it.

Claudio Demartini – Claudio Demartini is a professor at the Polytechnic University of Turin, Italy. His research interests are in the areas of software engineering, architectures, and web semantics. Currently, he is a member of the Academic Senate of Polytechnic University of Turin and a consultant on vocational education and training for the Ministry of University Research and Education. Demartini is a Senior Member of IEEE and the IEEE Computer Society. He has lead or co-lead several European projects, including some on education. Contact him at claudio.demartini@polito.it.

Andrea Sanna – Andrea Sanna is an associate professor at the Polytechnic University of Turin, Italy. He has published several papers in the areas of computer graphics, virtual reality, parallel and distributed computing, scientific visualization, and computational geometry. Sanna is currently involved in several national and international projects concerning distributed architectures and human-machine interaction. He is a Senior Member of ACM and serves as a reviewer for multiple international conferences and journals. Contact him at andrea.sanna@polito.it.

Paolo Montuschi – Paolo Montuschi is a professor of computer engineering at the Polytechnic University of Turin. His research interests include computer arithmetic and architectures, computer graphics, electronic publications, and new frameworks for the dissemination of scientific knowledge. Montuschi is an IEEE Fellow, an IEEE Computer Society Golden Core member, and serves as chair of the Computer Society’s Magazine Operations Committee, associate editor in chief of IEEE Transactions on Computers, and as a member of both the IEEE Transactions on Emerging Topics in Computing steering committee and of the Computing Now advisory board. He is also a member of the IEEE Publications Services and Products Board. Contact him at paolo.montuschi@polito.it.


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Translations by Osvaldo Perez and Tiejun Huang