In recent years, there has been a shift in how learning is being perceived. Combined with the rapid advances of information and communications technology (ICT) educational systems around the world are challenged on teaching designs and approaches to teaching and learning.

In 2010 The Organisation for Economic Co-operation and Development (OECD) published the report The Nature of Learning, focusing on “adaptive expertise”, “the ability to apply meaningfully-learned knowledge and skills flexibly and creatively in different situations” (Dumont & Benavides, 2010, p. 3). In 2012 the United States National Research Council highlighted a need for deeper learning and development of 21st century competencies. The concept of “deeper learning” is defined as “the process through which an individual becomes capable of taking what was learned in one situation and applying it to new situations (i.e., transfer)” (p. 20).

“Deep learning” (not to be confused with deeper learning) has found traction in the European educational context (Fullan, McEachen and Quinn, 2016), although with a wider definition: “learning that engages students in the mastery of academic content, creation of new knowledge, and development of deep learning competencies, all of which combine in the formation of actions and responses that drive their learning, their lives, and the world forward” (p. 2).

In Norway a curriculum reform, focuses on the concept of “in-depth learning”. In the Official Norwegian Report (NOU, 2015:8) in-depth learning is linked to the development of competence, “the capacity to use and apply knowledge and skills to master challenges and solve problems” (p.10) and that “the acquisition of competence requires in-depth learning” (p. 10).

The National Research Council (2012), Fullan et. al (2016) and the Official Norwegian Reports (NOU, 2015:8) all purport an addition to the term “learning”, be it deeper, deep or in-depth. To simplify reading this abstract the term deep learning will be used as term, which comprises both deep-, deeper- and in-depth learning.

A common theme concerning deep learning seems to be the ability to take knowledge and skills learned in one context and apply it to solve problems in a different context. This resonates with “adaptive expertise” fronted by the OECD.  There also seems to be a clear connection between these concept and “transfer” in a cognitive perspective. According to Nokes (2009), transfer is characterized as “how knowledge acquired from one task or situation can be applied to a different one” (p. 2).

However, because all of the publications discussed above (except Nokes, 2009) are grey literature, “unpublished, and thus less readily accessible, papers or research reports” (Polit & Beck, 2012, p. 729), further investigation is warranted to examine how the term is used in peer reviewed research literature. A systematic literature review of “deep learning” and other related terms will contribute with an overview of a field in the forefront of the current national and international educational political agendas. The aim of this systematic review is to get an overview of definitions and descriptions of the term deep learning, identify uses related to learning contexts, populations and measuring methods. The systematic review will try to answer:

1. How is the term deep learning presented and conceptualized in research literature?
2. How is the term deep learning related to other pedagogical and didactical terms?

Several terms seem to overlap in content/understanding/definitions of the term deep learning. Therefore, the following terms will also be included in the systematic literature search to see if, and how they relate to deep learning: “transfer” (Nokes, 2009), “deep level processing” (Marton & Säljö, 1976) and “21st century competencies” (National Research Council, 2012).

This literature review draws on well-known procedures defined in the literature on research synthesis (Davies, 2000; Thomas & Pring, 2004; Gough, Oliver & Thomas, 2017). More precise, the study shares similarities with systematic mapping and narrative analysis in which different elements extracted from individual studies are identified and configured into a new, macro-conceptual and/or theoretical understanding (Gough et al., 2017). A protocol for systematic literature search has been developed using the PRISMA-P checklist (Moher et al., 2015). PICo (Population, Interest, Context) were used to structure the search. The population is defined as children and youths attending school. The interest is deep learning, or pedagogical or didactic terms related to deep learning. The context is defined as school. The search has been conducted in three databases, Education Resources Information Center (ERIC), Education Source and Scopus. The search string was adapted to suit the different databases, but the text words were the same in each search in each database. The search included the text words found in title (TI), subject descriptions (SU), key words (KW), and abstract (AB). Search results was narrowed down by limiting the search to the time period 1970-2018 and to peer-reviewed articles. Results were then exported to EndNote and checked for duplicates by going through each potential duplication manually. The remaining articles was then exported to the software Rayyan QCRI (Ouzzani et al., 2016). The abstracts were screened by two independent researchers using an inclusion template. To test and establish inter-rater reliability and calibrate understanding of inclusion criteria the first 100 abstracts (in alphabetical order) were screened and discussed to compare inclusion and exclusion. In the next phase the included studies will be read full-text and analysed using a thematic template.

Preliminary results: The search resulted in 812 hits in the database ERIC, 614 hits in the database Education Source and 415 hits in database Scopus, in total 1841 hits. Duplication check lead to the removal of 524 duplicates. The result, after removal of duplicates, were 1317 potentially relevant articles. Expected outcomes: We will get an overview of how the term deep learning is understood in research literature, both through different definitions, how research connected to it is designed, which methods have been used and what findings that are reported. The impression so far is that deep learning is used quite frequently in recent research literature, but the use of the term can be described as spread on a continuum ranging from “clearly defined” to “hype word” just describing learning that is meant to exceed “regular” learning. First impressions of the literature search seems to indicate a strong relation to mathematics. Another point of interest would be the relationship between the term deep learning and ICT. There seems to be quite a lot of research connected to the use of computer-games with explicit learning content and school.

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