Long-Distance Interdisciplinarity Leads to Higher Scientific Impact

Scholarly collaborations across disparate scientific disciplines are challenging. Collaborators are likely to have their offices in another building, attend different conferences, and publish in other venues; they might speak a different scientific language and value an alien scientific culture. This paper presents a detailed analysis of success and failure of interdisciplinary papers—as manifested in the citations they receive. For 9.2 million interdisciplinary research papers published between 2000 and 2012 we show that the majority (69.9%) of co-cited interdisciplinary pairs are “win-win” relationships, i.e., papers that cite them have higher citation impact and there are as few as 3.3% “lose-lose” relationships. Papers citing references from subdisciplines positioned far apart (in the conceptual space of the UCSD map of science) attract the highest relative citation counts. The findings support the assumption that interdisciplinary research is more successful and leads to results greater than the sum of its disciplinary parts.

Introduction
Long-distance relationships are tough. Scholarly collaborations across disparate scientific disciplines are even more challenging: Collaborators are likely to have their offices in another building, attend different conferences, and publish in other venues; they might speak a different scientific language and value an alien scientific culture. Over the last 20 years, interdisciplinarity has remained a hot topic in science policy [1][2][3]. Interdisciplinary is formed through problems, it is thus aimed at application, and can be defined as the combination of methods, theories and data of distinct disciplines to ideally derive a result that is greater than the sum of its parts [4]. As it is often taken for granted that it is a fruitful enterprise, governments and international organizations have pushed towards interdisciplinary research [5,6]. However, previous research provided conflicting evidence on its effect on the impact of research. For instance, at the level of research groups, Rinia, van Leeuwen and van Raan showed, using the percentage of papers published outside of physics as a measure of Dutch physicists' interdisciplinarity, that interdisciplinary programs had lower impact (raw citations and raw impact factors) than programs that were at the 'core' of physics [7]. Applying relative impact indicators, the differences in impact between the groups were, however, lower. At the level of journals, Levitt and Thelwall provided evidence that, in the natural and medical sciences, papers published in journals to which more than one disciplinary classification had been assigned obtain fewer citations than papers published in strictly disciplinary journals [8]. Adams, Jackson, and Marshall calculated for 37,000 papers from two UK universities, the percentage of references made to journals published in disciplines other than that of the citing paper and found that interdisciplinary papers were not cited more than the 'average' paper, and that highly-cited papers were not the most interdisciplinary ones [9]. Also at the level of individual papers and using the percentage of references made to journals of a different discipline, Larivière and Gingras showed that, for some disciplines-such as biomedical research-an increase in interdisciplinarity was associated with a decrease in citations [10]. For some other disciplines, a moderate interdisciplinarity was associated with an increase in impact. In all disciplines, however, papers with very high or very low interdisciplinarity obtained fewer citations; a finding which has recently been corroborated by Yegros-Yegros et al. at the level of research groups [11]. Larivière and Gingras' [10] results also show that the relationship between interdisciplinarity and impact was "highly determined by the citation characteristics of the disciplines involved, as articles citing citation-intensive disciplines are more likely to be cited by those disciplines and, hence, obtain higher citation rates than are articles citing noncitation-intensive disciplines" (p. 131). This has important consequences on the interpretation given to the relation between citations and interdisciplinarity, as it shows that the disciplines involved and their citation dynamics can influence the scientific impact of interdisciplinary relationships [10]. Recent work by Uzzi et al. rates the novelty of journal pairs in reference lists equate it with a possible novelty of results presented in the paper, and correlate it with the ultimate citation-impact of papers [12]. They find that novel combinations, when interjected with otherwise conventional combinations, yield increases in impact. Similarly, Klavans and Boyack [13] showed, using clusters of papers rather than disciplines, that the impact of citing papers increases with the topical distance of the papers they cite.
Despite these numerous analyses, the effect of long-distance interdisciplinarity on the impact of research remains open to debate. Similarly, while the relationships between disciplines have been extensively mapped and visualized [14][15][16], these are typically made to assess their intensity rather than their effect on the scientific impact (citations) of citing papers. This paper, thus, aims to measure and visualize subdiscipline distances and linkages to answer the following questions: What are the (sub)disciplines that increase impact when cited together? To the opposite, what are the interdisciplinary combinations that are associated with a decrease in citations? The visualization of these interdisciplinary combinations and their effect on the impact of papers will shed light on the network of (sub)disciplines that have a positive effect on the impact the papers citing them. This analysis is limited to the scientific impact of interdisciplinary research, as measured by citations received. Other types of impacts which interdisciplinary research may have on education, industry practice or society, health or the economy at large are not measured here. The results nonetheless shed light on the impact of interdisciplinary relationships in the scientific community, and may trigger more analyses-either quantitative or qualitative-on what it is exactly in these combinations of disciplines that result in highly cited research. This, in turn, can have significant implications for research policy and institutional planning.

Number of subdisciplines cited and citation rates
The relative citation rate-both in terms of average and media-of papers increases with the number of subdisciplines they cite, supporting the perceived success of interdisciplinarity (Fig 1, panels A and B). At citation impact below the expected number of citations of papers published in the same subdiscipline at the same year, strictly disciplinary papers, i.e., those citing papers from one subdiscipline only, obtain the lowest citation rates (0.66). These disciplinary papers make up 17.2% of all papers in the WoS. Papers citing 3 subdisciplines or less obtain a mean impact below world average (47.3% of all papers), while papers citing 4 subdisciplines (11.5% of all papers) obtain a mean citation rate that is on a par with the mean impact of papers published the same year in the same subdiscipline. On average, citation rates exceeding the world average of 1 are obtained by papers citing references from at least 5 different subdisciplines (41.2% of all papers). A similar trend is observed for the median-although the impact values are lower, as they are less likely to be influenced by a few highly cited papers.

Citation impact of interdisciplinary combinations
The mean relative citation rate of all interdisciplinary subdiscipline pairs included in this study is 1.54. Out of all 161,994 interdisciplinary subdiscipline pairs, 69.9% are win-win relationships, 26.8% increased impact for one of the subdisciplines, and only 3.3% do not exceed expected citation rates in any of the two subdisciplines (Fig 2). Note that the majority of interdisciplinary papers are win-win-this is due to the fact that strictly disciplinary pairs typically have an impact below average, as well as to the various interdisciplinary pairs having different numbers of papers. With 85% win-win pairs, interdisciplinary relationships by and with subdisciplines from Chemistry are most often beneficial, followed by Brain Research (78.8%) and Biology (76.9%). Most lose-lose relationships occur with subdisciplines in the Humanities (11.2%) and Electrical Engineering & Computer Science (7.3%). From the perspective of a particular discipline, the Humanities are by far benefiting most from interdisciplinary pairs but other disciplines involved frequently lose, i.e., 44.5% of all Humanities subdisciplinary pairs are win-lose combinations. In terms of beneficial combinations, interdisciplinary co-citations that include subdisciplines from Biotechnology, Chemistry and Brain Research are most often successful as respectively 97.0%, 96.4% and 96.4% of all subdisciplinary combinations are either a win-win or lose-win, i.e., the citation impact of both or of the co-cited subdiscipline increased. Distinguishing between intradisciplinary, i.e., pairs of two subdisciplines belonging to the same discipline, and truly interdisciplinary pairs, the most interdisciplinary win-win pairs are in Chemistry (77.7%) and Biotechnology (73.2%), while Brain Research (16.0%), Chemical, Mechanical, & Civil Engineering (12.9%) and Medical Specialties (12.6%) show the highest percentages of intradisciplinary win-win pairs.

Citation impact and topical distance
The mean relative citation rate of co-cited interdisciplinary pairs constantly increases with the distance between them ( Table 1 and Table 2). Category A, which contains co-cited subdiscipline pairs closest to each other in the UCSD map-in fact 71.4% of the pairs in this category were assigned to the same discipline-has both the lowest citation rate (1.26) and lowest share of win-win pairs (62.4%), while category J, which at a mean distance of 205 contains the topically least related subdiscipline pairs was on average cited 72% above average. Except for distance categories A and J, the percentages of win-win relationships are quite stable at around 71%. This suggests that the increase in citation impact is not caused by an increase in the number of win-win pairs but rather by an actual growth of relative citation impact as distance increases. In other words, the probability of an interdisciplinary relationship increasing a paper's relative citations in relation to both subdisciplines is similar across distance groups B to J but impact intensifies with greater distance. However, both medians (1.48-1.50) and standard deviations (0.79 to 0.97) of categories G to J suggest that the increasing citation rates in these four groups are less due to an overall increase but mainly due to a few highly cited pairs ( Table 2). Fig 3 shows the number of co-citing papers as well as the relative citation rates across distance categories A to J for each of the 13 disciplines. Relative citation rates across distance Table 1. Number of interdisciplinary pairs per discipline, mean distance and mean, standard deviation and ratio of distant (F-J) and close (A-E) subdiscipline pairs of relative citation rate.  Chemistry show only a small increase of citation rates. It should, however, be noted that for none of the 13 disciplines impact decreases with increasing distance. Table 3 shows the number and percentages of win-win and lose-lose relationships of interdisciplinarity pairs in the UCSD map of science. Thus it highlights where the most successful and unsuccessful combinations of co-cited subdisciplines are located in the landscape of scientific disciplines. Relationships which involve one winner and one loser (win-lose, lose-win) are not shown. On average, the percentage of win-win relationships is highest in Chemistry (mean: 85.0%; std. dev.: 6.9%) and Brain Research (78.4%; 12.6%) and lowest in the Humanities (35.0%; 23.0%) and Social Sciences (50.4%; 13.9%), which is likely a consequence of the lower citation density of those disciplines. On the lose-lose side, Nursing Theory (Medical Specialties) was involved in the most lose-lose relationships, both in absolute (62 lose-loses) and relative (19.6%) terms and the Humanities (23.7%; 26.5%) are involved in the most lose-lose relationships, on average. Subdisciplines associated with Biotechnology had, on average, the lowest share of lose-lose pairs (1.1%; 1.1%), which clearly suggests that its multiple application to life sciences, pharmacology, agriculture or engineering is beneficial to all subdisciplines involved.

Mapping interdisciplinarity
The heat map (Fig 4) visualizes the citation impact of all 80,997 occurring subdiscipline pairs s 1 -s 2 from the perspectives of both s 1 (row) and s 2 (column). For example, the majority of red cells in the Social Sciences (row) > Brain Research (column) field indicate that these interdisciplinary connections are mostly beneficial relative to expected citation rates in the Social Sciences, while the large number of blue cells in the Brain Research (row) > Social Sciences (column) field shows that from the perspective of Brain Research, many of these pairs do not result in high citation impact. The majority of red cells in the matrix reflects the overall success of interdisciplinary pairs. The heat map also highlights where interdisciplinary relationships do not deviate much from expected citation rates or do not appear at all (white color coding), for example between the Humanities and Chemical, Mechanical, & Civil Engineering.    subdiscipline pairs occurring most frequently are close to each other and mostly belong to the same or neighboring disciplines (color-coded). The most frequently co-cited win-win pair is Protein Science (Biotechnology) and Clinical Cancer Research (Brain Research), which occurred in 562,384 citing papers and focuses, among other topics, on the genetic basis of cancer. Loselose relationships appear much less frequently and often between subdisciplines belonging to distant disciplines. The most frequently occurring lose-lose pair is Food Engineering (Biotechnology) and Food Chemistry (Chemistry) (52,342 papers cite works from these two disciplines). The node size in A1 and B1 indicates the number of win-win and lose-lose relationships for particular subdisciplines. The "biggest winners" and "biggest losers" of interdisciplinarity are labeled for each discipline. Comparing A1 and B1, it can be seen that the number of wins are less skewed and more equally distributed across the science map than the number of losses. The subdiscipline with the most win-win relationships in absolute numbers is Molecular Ecology (Biology; 409 win-wins, 81.2% of all its interdisciplinarity relationships). However, Material Science (Chemical, Mechanical, & Civil Engineering; 387; 91.9%) obtained more win-wins as a percentage of all its interdisciplinarity combinations (Table 3).
Node size in A2/B2 and A3/B3 indicates the mean citation rate of all outgoing (A2/B2) and incoming (A3/B3) pairs. As such, the maps show which subdisciplines gain and lose. While the mean of outgoing citation links (mean of s 1 -s n for s 1 ) highlights recipients of impact, the mean of incoming links (mean of s n -s 1 for s 1 ) marks the donors, who provide most positive (winwin) or negative (lose-lose) impact to the co-cited subdiscipline. As shown in A2, the greatest recipients of win-win relationships belong to the Humanities, Electrical Engineering & Computer Science and Math & Physics, with Art History (mean incoming relative citation rate: 10.23) and Biblical Literature (8.02) benefiting the most. Donors are more equally distributed and appear in different disciplines with Industrial Chemistry (2.80), Biotechnology Trends (2.67) and Bulk Solid Handling (2.66) providing on average the highest citation rates to the co-cited subdiscipline. On the lose-lose side, the picture on the disciplinary level is reversed: Recipients of highest lose-lose relationships are equally distributed with Modern Language (mean incoming relative citation rate: 0.37 = 63% below expectations), Gut Maps A4 and B4 highlight the strongest connections between two subdisciplines in terms of relative citation rates. In A4, arcs with a citation rate of 5.0 and higher are shown, while B4 depicts citation impact of 0.5 and lower, i.e., 50% below expectations. Node size indicates the number of win-win and lose-lose relationships and is thus identical to A1 and B1. Arcs move clock-wise from source to target and are colored according to the source. For each discipline, the strongest outgoing connection is labeled. The highest relative citation rate is obtained by Child Abuse when co-cited with Leukemia (27.53) and the lowest impact by Pragmatics & Discourse when co-cited with Modern Language (0.09). It can be seen that the strongest win-win combinations (links) are between the Social Sciences to Brain Research (e.g., Child Abuse to Leukemia, 27.53), Brain Research to Earth Sciences (e.g., Thoracic Surgery to Air Quality, 27. 22

Discussion and Conclusion
Since the 1980s, interdisciplinary relationships have increased [17], likely because of "government programs and discourses promoting interdisciplinarity as a good thing in itself" (pp. 197-198). Our results provide evidence that, on the whole, interdisciplinarity research, as measured by co-citation links, is beneficial, as the majority of co-cited interdisciplinary pairs benefit in terms of citation impact, and as few as 3.3% of represent losses for both subdisciplines involved. While most win-win relations are associated with disciplines related to medicine, most lose-lose relationships are associated with disciplines that typically have lower citation rates, such as computer science and engineering, social sciences and the humanities. This might also be due to the lower coverage of literature published in these domains, which often appears in non-journal literature (conference proceedings, books, etc.).
Topical distance has a positive effect on scientific impact in the natural sciences and social sciences and to a lesser extent in medical disciplines. This might be caused by several factors, two of which are discussed here. First, combining methods and perspectives from multiple disciplines might be able to solve scientific problems or social issues that are more complex and that one discipline alone cannot solve [4]. Second, different subdisciplines have diverse intrinsic citation dynamics. As shown by Larivière and Gingras [10], papers' citation rates are influenced by the disciplines they cite: a humanities paper citing papers in medicine is likely to obtain higher-than-average citations compared to other humanities papers, while a medical paper citing papers in humanities is likely to experience the opposite faith. This would explain why the humanities, as well as other low citation count disciplines, are more often involved in lose-lose and win-lose relationships.

Materials and Methods
Disciplines and subdisciplines were assigned to journals using the UCSD map and classification system of science [18]. This classification covers 10 years (2001-2010) of WoS data and 8 years (2001-2008) of Scopus data and fractionally matches about 25,000 journal and other source titles to 13 disciplines and 554 subdisciplines using a combination of bibliographic coupling and keyword co-occurrences as similarity measures on journal level. The distance between two subdisciplines is calculated using the x-y positions of the 554 subdisciplines on the UCSD map. Note that this map wraps around a cylinder, i.e., the left most nodes are connected to the nodes on the far right. The cylinder circumference is 624 and the maximum distance is half of this: 312. The distances for the 153,181 subdiscipline pairs range from 0.63 to 294.32.
This study uses a definition of interdisciplinarity based on cited references. An interdisciplinary relationship between 2 of the 554 UCSD subdisciplines exists, if two papers from these subdisciplines are cited together in a third paper. Co-citations measure the contextual similarity between two references [19] as "the degree of relationship or association between papers as perceived by the population of citing authors" (p. 265). If aggregated on the level of scientific disciplines and subdisciplines, it thus reflects the combination of resources from different (sub) disciplines by the authors of the paper. Co-cited subdiscipline pairs were extracted from the reference lists of citing papers using a binary approach, i.e., counting each pair once per citing paper, irrespective of the number of papers associated with the particular subdiscipline in the reference lists of the citing paper. Hence, if a paper A cited five references in ophthalmology, three in astrophysics, and ten in zoology, then three co-citation links were created based on paper A: ophthalmology-astrophysics, ophthalmology-zoology and astrophysics-zoology. The frequency of occurring subdiscipline pairs equals the number of papers co-citing papers from the two particular subdisciplines. Each co-cited interdisciplinarity pair was assigned its distance on the UCSD map to represent its topical proximity. Pairs were grouped into 10 categories of distances (A-J) with a comparable number of pairs in each category.
The dataset used in this paper is drawn from Thomson Reuters' WoS database, including the Science Citation Index Expanded (SCIE), the Social Sciences Citation Index (SSCI) and the Arts and Humanities Citation Index (AHCI). It comprises 11.1 million papers (articles and reviews) published between January 1, 2000 and December 31, 2012 and their cited references matched to the UCSD classification system via journal name. Only references from this period were considered, as the UCSD classification system builds on 2000-onwards publications. This leads to a lower proportion of references covered for the older years, and to a higher proportion for the more recent ones. Given that the analysis is based on the entire 13-year period instead on an annual basis, the different coverage of the specific years should not affect the results. References were linked to their record as a source item using Thomson's ItemID_ref as well as inhouse matching using the first author, publication year, volume number, and page number. The match of cited references with the source item allows assigning a discipline and subdiscipline to each cited reference. We excluded the 40 journals that are fractionally assigned to more than one UCSD subdiscipline to avoid artificially creating subdisciplinary pairs due to double classification. Based on this assignment, a paper is defined as interdisciplinary if it contains references from more than one subdiscipline. 17.2% of the 11 million papers in the database cited references of one subdiscipline only.
On the whole, 70.0% of all references made were matched to source items indexed in the WoS, 45.8% of which were published in the period analyzed. A total of 138,657,550 or 32.1% of all cited references were matched to a UCSD subdiscipline and were taken into account for the calculation of citation impact of co-cited interdisciplinary pairs. The percentage of references matched to source items and published between 2000 and 2012 varied among disciplines, given the different use of journal literature [20,21] and age of references. The percentage of references made to WoS-indexed items was highest in Biotechnology (84.6%) and lowest in Humanities (7.0%) on the level of disciplines and if limited to the publication years under analysis, highest in Infectious Diseases (39.9%) and Brain Research (39.6%) and lowest in Social Sciences (16.9%) and in the Humanities (2.4%). At the higher end of the spectrum for subdisciplines, more than 50% of references were matched with a source item and published between 2000 and 2012 in Molecular Biology Methods (56.2%), Nanotechnology (54.8%), Proteomics (54.1%), Cancer (translated; 54.0%) and Clinical Medicine (translated; 52.15), while this percentage remained below 1% in Literary Criticism, Hispanic Studies, Italian Studies, Art History, Textile Art, Medieval History, and Modern Language.
The final dataset of interdisciplinary papers contained a total of 9,166,710 papers and their cited references matched to a UCSD subdiscipline. All unique subdiscipline pairs (see binary definition above) were extracted from each of the papers. A threshold of 30 co-citing papers at the level of subdisciplines during the 2000-2012 publication period was applied to assure a minimum robustness of citation rates. Of the 153,181 possible pairs of the 554 UCSD subdisciplines, 80,997 (52.9%) were co-cited by at least 30 papers 179 million times amounting to a mean number of 2,214 co-citing papers per subdiscipline pair. As to be expected, the number of co-cited papers per interdisciplinary pair is positively skewed and the average number of cocitations per pair decreases continuously per distance categories A to J, i.e., the more distant the two disciplines the smaller the number of co-citing papers.
Two normalization methods are performed in this paper. A first one, which is used in Fig 1, takes the subdiscipline (and year) of the journal in which the paper is published as the denominator, and then divides each paper's observed number of citations by this expected value. In this case, each paper only has one normalized citation rate, and a clear zero-sum game at level of all paper is observed. A second one, used to measure the impact of interdisciplinary relationships, is performed at the level of the paper and subdiscipline combination. In this case, each pair obtained two relative, i.e., observed vs. expected, citation scores normalized by the average citation rates of all papers-interdisciplinary or not-that cited each of the two subdisciplines. Citations of each citing paper were counted from for the entire period studied, 2000-2013. This normalization procedure was applied to account for the fact that the citation impact of interdisciplinary papers depends on the citation potential of the involved subdisciplines [10]. The impact of a paper p co-citing subdisciplines s 1 and s 2 needs to be calculated in the citation context of both s 1 and s 2 separately. The observed citation score of s 1 -s 2 represents the citation score of p. The expected citation rates for each co-cited subdiscipline are based on the citation rate of all papers citing the particular subdiscipline in the same publication year to normalize for age. In this normalization approach, the expected citations of the subdiscipline are defined by its direct citation environment. For subdisciplines s 1 and s 2 co-cited by paper p in year y, the expected citation rate of subdiscipline s 1 is thus the average citation rate of all papers citing subdiscipline s 1 in year y. The relative citation rate of the interdisciplinary pair s 1 -s 2 relative to s 1 equals the average of all observed vs. average ratios on the document level of all co-citing papers in all years, conforming to the average-of-ratio method [22]. The relative citation rate of the interdisciplinary pair s 1 -s 2 relative to s 1 is based on the expected citations for s 1 . Two distinct relative citation rates were computed for the 80,997 occurring subdiscipline pairs, resulting in an asymmetric matrix with 161,994 relative citation rates. Thus, each co-cited pair s 1 -s 2 obtained two distinct observed vs. expected citation rates, normalized by the citation rates of papers that cited the subdiscipline s 1 and s 2 , respectively, in the same year. Based on the pairwise comparison of the relative citation rates of co-cited subdisciplines, the success of interdisciplinary relationships was defined as win-win, win-lose/lose-win and lose-lose citation outcomes. If, on average, papers co-citing subdiscipline pair s 1 -s 2 were cited above expectations in both s 1 and s 2 the interdisciplinary relationship s 1 -s 2 was defined as a win-win relationship in terms of observed vs. expected citation impact, while a lose-lose situation was at hand if it did not exceed expected citation impact in both s 1 and s 2 . If citation impact was above expectations compared to s 1 but below average compared to s 2 or below s 1 and above s 2 , the interdisciplinary relationship s 1 -s 2 was defined as a win-lose or lose-win connection, respectively, see Fig 6 and examples therein. While a zero-sum can be obtained here at the level of paper and cited subdisciplines combinations, it cannot be observed for subdiscipline pairs, as papers citing n subdisciplines appear n 2 -n times (because the diagonal is empty) and each subdiscipline pair is based on a different number of papers. While the first normalization method is used to show the scientific impact of papers citing a certain number of subdisciplines (i.e. level of interdisciplinarity), the second is used to assess the scientific impact co-cited subdisciplines.
Success of interdisciplinarity in terms of citation impact is visualized using a heat map (Fig 4) and network graphs (Fig 5). The heat map shows the mean relative citation impact of subdiscipline pairs, i.e., each cell of the matrix represents the mean relative citation impact of a particular pair. The asymmetrical square matrix contains 554 rows and columns representing the subdisciplines of the classification, which are grouped according to the 13 superordinate disciplines. Cells representing subdiscipline pairs with citation rates above world average are indicated by shades of red, those below by shades of blue, while citation rates close to world average are colored in white. About half of the matrix cells remain empty because the respective interdisciplinary pair did not meet the threshold of 30 co-citing papers or they were strictly disciplinary (i.e., the diagonal of the matrix). The empty cells are also colored in white and can thus not be distinguished from cells with citation impact close to world average. Hence, the heat map highlights the winners and losers of interdisciplinary relationships from the perspective of both s 1 and s 2 .
While the heat map (Fig 4) represents the 161,994 observed vs. expected citation impacts of interdisciplinary relationships of subdiscipline pairs, the win-win and lose-lose relationships with the highest citation impact are visualized in the UCSD map of science (Fig 5) in order to show which interdisciplinary combinations are most successful and to communicate the distances of the subdisciplines involved, see Fig 5 A4 and B4.


extfieldsofstudy:
Computer Science
Psychology

text:
Empathy, Special Education and ICTs

During the past decades there have been considerable innovations with the use of Information Communication Technologies (ICTs) in many fields and sectors. In many settings ICTs have become an important element of the learning and teaching process. All social groups have been helped including children and adults with special educational needs. In this paper we provide a brief overview of the most representative articles for applications used for assessment, intervention and development of empathy which is an important skill that researches indicate that people with special educational needs lack of it. Empathy is a complex form of psychological inference in which observation, memory, knowledge, and reasoning are combined to yield insights into the thoughts and feelings of others. Index Terms – ICTs, Empathy, Special Education, Emotion Recognition

INTRODUCTION
In today's society, the use of information and communication technologies (ICTs) is a regular part of most people's lives. The term Information and Communication Technologies is a general term which refers to all kids of technologies that enable users to access and manipulate information [1]. ICTs have been widely studied in a large number of fields as well as being a subject of study of its own right. In the field of education, more and more tools making use of new technologies are being released to support teaching. Furthermore, there has been a growing number of researches that supports the fact that, ICTs and assistive technologies more generally, enable children and people with special educational needs to lead more fulfilled lives. Computer applications can be helpful in reaching this objective.
The use of Information and Communication Technologies (ICTs) with individuals with special educational needs (SEN) has !ourished in the last decade for several reasons: the computerized environment is predictable, consistent, and free from social demands, which individuals with SEN may "nd stressful. Users can work at their own pace and level of understanding, and lessons can be repeated over and over again, until mastery is achieved. In addition, interest and motivation can be maintained through different and individually selected computerized rewards. For these reasons, and following the af"nity individuals with SEN show for the computerized environment, dozens of ICT programs, teaching various skills to this population were created. In the last decade, with the rapid development of internet based communication, web applications have been increasingly used for social interaction, forming online communities and social networks [2].
The term 'special educational needs' covers a range of problems which can cause difficulties in learning.Even though there have been many definitions over the years, comparative studies show that the term 'special educational needs' can mean different things to different countries [3]. One of the most dominant categorizations that recommends a graduated approach to educating learners in need of special provision is the one introduced below. 'The areas of needs' as defined in the 2001 SEN Code of Practice are: Communication and Interaction, Sensory and/or Physical, Cognition and Learning, Behavior, Emotional and Social Development [4]. Defining the area of 'Special Needs' has been a widely discussed issue. Titles as Learning Disabilities or Learning Disorders are also used to describe a group of the population that have problems in their school performance and maybe later in their lifetimes. In this paper we will focus on empathy and its deficit which has consistently been cited as a central characteristic of people with special educational needs.
Empathy allows us to understand the feelings of others, predict their behaviors, and experience emotions triggered by their emotions. Empathy is manifested in emotions and perceptions triggered by an individual's attraction to external feelings. Baron-Cohen and Weelwright (2004) de"ne empathy as having two components: the ability to recognize other people's mental states (such as intentions, beliefs, desires and emotions), and the ability to respond to these with an appropriate emotion. This two-factor de"nition of empathy sidesteps the traditional debate about whether empathy is a wholly emotional response (the emotion triggered by another person's emotions) or an entirely cognitive operation (the ability to put oneself into another person's shoes and perceive the world from their point of view). In line with classic models of empathy such as Feshbach's model, in which empathy is conceptualized as entailing cognitive and affective processes, the current consensus among scholars leans towards recognizing both components as necessary to de"ne empathy [5], [6].
ICTs offers many chances to find innovative ways of teaching empathy and emotion recognition to children and people with special educational needs. Research has shown that computerized programs can be used to pass false belief tasks, recognize simple emotions from static photographs and cartoons, identify complex emotions from facial expressions and prosody of speech and assess, intervene and cultivate empathy through virtual environments and other software. Assistive technology offers socially impaired individuals an environment in which they can learn the meaning of emotions and understand more about the way they communicate with their peers.
The purpose of the current paper is to present an overview with the most representative studies that deal with computer interactive intervention programs in order to assess and develop empathy in children and adults who have special educational needs. This paper indicates that practicing simulated activities on the computer enhances PAPER EMPATHY, SPECIAL EDUCATION AND ICTS facial and emotion recognition abilities. The results provide support for the effectiveness of using a computerbased interactive simulation program as a vehicle for enhancing empathy and emotion recognition skills. Technologies and sensors that can sense emotions or express and thereby influence users' emotions have been continuously developed but there are enough studies that use instruments such us well -established questionnaires, scales and photographs in order to export results, measure and evaluate empathy.

A. Learners with sensory impairments
Griffin-Shirley et al. (2005) conducted a study in order to compare the self-esteem and empathy of preadolescent children with visual impairments with those of sighted children. Several family variables were explored. Of particular interest to this study was the variable of pet ownership. Instruments included the CSEI-Short Form, the Bryant Index of Empathy for Children and Adolescents (IECA), and the Companion Animal Bonding Scale (CABS) were used to extract results. The study has been tested on 159 participants (81 boys and 78 girls) included two groups of preadolescents--71 who were visually impaired and 88 who were sighted--who ranged in age from 8 to 14. Sighted and visually impaired participants were recruited from two southwestern states, two midwestern states and one western state form the United States. No significant differences were found between the sighted and visually impaired children for levels of self-esteem, empathy toward others, or taking care of pets [7]. Odom et al. (1973) examined empathy in deaf and hearing children and their ability to interpret and evaluate emotion-arousing situations. Drawings were shown by an opaque projector and two tasks were given: (a) sorting faces portraying nine emotions and (b) matching those faces with drawings of appropriate emotion-arousing situations. The deaf children performed as the hearing children did on the first task but did not match the faces to the situations as well as the hearing children. It was concluded that the deaf have difficulty analyzing and interpreting emotion-producing situations. The lack of emotional empathy in the deaf may well be a model instance of a relationship between language training and an important dimension of personality [8]. Cummins et al. (2005) investigated whether children with poor motor coordination have empathic abilities, and whether these could be connected to problems in moral judgement, pro -social behaviors and social competence. The research included emotion recognition scales that measured the ability to recognize facial expressions of emotions (The Fluid Emotions test), to understand vocal intonations specifically for different emotions (The Vocal Cues test), to define emotion words (The Emotion Vocabulary test), to understand the emotional consequences of exposure to an emotion that causes context (The Comprehension Test) andto implement logic skills and knowledge of the causes of emotions to explain apparent discrepancies between one emotion that is the context and the emotion caused by the frame (The Unexpected Outcomes test). This study has been tested on 234 children aged 6-12 years matched for age and gender and the results indicated that children with motor coordination problems do have specific deficits in empathy [9].

C. Learners with Autistic Spectrum Disorders
Silver and Oakes (2001) investigated the use of a multimedia software program, the Emotion Trainer, to teach individuals with ASD to recognize and anticipate emotions in others. The Emotion Trainer had five sections and utilized photographs of real people, as well as animated emotional expressions, to teach about emotions. Consistent feedback, prompting and reinforcement were provided and were contingent upon the level of success or dif"culty an individual experienced while progressing through the program. Twenty-two individuals with ASD, ranging from ages 10 to18, were matched based on age, gender, and school class. One member of the pair was randomly assigned to the intervention condition of 10 computerized sessions over 2-3weeks, while the second member was placed in the no-intervention control condition. Both groups showed signi"cant improvements in the ability to identify emotion or mental state from photographs of facial expression from pre-to post-intervention [10]. Golan et al. (2010) created an animated series called "The Transporters" to help children with autism between the ages of three and eight to look at the human face and to understand and recognize emotions that contribute to the development of empathy. The series of 15 five-minute episodes features the adventures of 8 animated mechanical vehicles with human faces, each focusing on a different human emotion. Such vehicles would grab the attention of both preschoolers with autism and those so -called "low -functioning'' children with autism with significant learning difficulties. All of the characters were depicted as toys in a child's bedroom which come alive and take part in social interactions. Children with ASC (aged 4-7) watched "The Transporters" every day for 4 weeks and tested before and after the intervention. Results indicated that using "The Transporters" signi"cantly improves levels of emotion comprehension and recognition [11]. Blocher et al. (2002) designed "The Affective Social Quotient project" which is one of the early projects at MIT Media Lab to develop assistive technologies for autism using physical input devices, namely four dolls (stuffed dwarfs), which appeared to be happy, angry, sad, or surprised. The system would play short digital videos that embody one of the four emotions, and then encourage the child to choose the dwarf that went with the appropriate emotion. When the child picked up the stuffed toy, the system identified its infrared signal and responded. Use of the dolls as physical input devices also encouraged development of joint attention and turn-taking skills, because typically another person was present during the session [12].  introduced the gaming platform ASC Inclusion targeted to children aged 5 to 10 years with Autistic Spectrum Disorders. The running ASC-Inclusion project aims to help children with ASC by allowing them to learn how emotions can be expressed and recognized via playing games in a virtual world. The platform includes analysis of users' gestures, facial, and vocal expressions using standard microphone and web-cam or a depth sensor, training through games, text communication with peers, animation, video and audio clips [13]. PAPER EMPATHY, SPECIAL EDUCATION AND ICTS Serret (2012) developed a serious game, "Jestimule", to improve social cognition and empathy in ASD. ICT was also used to facilitate the use of the game by young children or by children with developmental delays (e.g., haptic joystick for feed-back). One of the main aims of the game was to teach ASD individuals to recognize facial emotions, emotional gestures and emotional situations. The game was tested on a group of 40 individuals (aged form 6 to 18) at the hospital. Results showed that participants improved their recognition of facial emotions, emotional gestures and emotional situations in different tasks. These results have clear education and therapeutic implications in ASD and should be taken into account in future training [14]. Bölte et al. (2002) developed and assessed a computerized intervention aimed at teaching basic facial affect recognition skills to individuals with ASD. The objective of this program is to diagnose and support the development of social-communicative skills through the assessment and training of elementary emotion perception and interpretation. Five hundred photographs, each with visual and audio feedback capabilities, were used to train individuals to recognize emotions. After providing feedback about a given picture, an animation of the corresponding emotion was presented on the screen as reinforcement. The program was tested on individuals with highfunctioning autism or Asperger-syndrome by showing them the whole face or just the eyes of the persons in order to identify the emotions. Results support the usefulness of the program to teach the detection of facial affect. However, the improvement found is limited to a circumscribed area of social-communicative function and generalization is not ensured [15]. Konstantinidis et al. (2009) created an Affective Computer-Aided Learning Platform for Children with Autism (ACALPA) to enhance or mediate the teacher-child educational process. It is based on various interaction procedures according to the disability level of the autistic person in question. Several modules comprise the platform. Each of them represents a specific learning domain. One module is related to identify emotional states by visual expressions. The instructions are given either by an affective avatar, synthesized speech in autistic person's native language, written in the screen, or a combination of these. ACALPA was implemented in a specialised school for people with autism and results indicated that the platform played an important role in emotion recognition and that an interactive learning environment might facilitate the educational procedure for people with autism [16]. Alves et al. (2013) presented the LIFEisGAME prototype-Ipad version which is a game that promotes facial recognition and helps individuals with ASD to understand emotions in order to develop empathy using real-time automatic facial expression analysis and virtual character synthesis. It includes five games modes. LIFEisGAME prototype was tested on 11 children with ASD, with ages varying from 5-15 years old and was played during a 15 minute game session. The results were promising and indicated the usefulness of the game to promote emotional understanding, bringing positive outcomes to quality of life for children with autism [17]. Kaliouby et al. (2005) presented "The emotional hearing aid" -a portable assistive computer device to help children diagnosed with Asperger Syndrome read, understand and react to facial expressions in a socially -appro-priate way facilitating the process of empathy. It consists of a personal digital assistant (PDA), an earpiece speaker and a wearable camcorder. In developing the emotional hearing aid an automated mind -reading systems that infers complex mental states from facial expressions in real -time video, and a reaction advisor that suggests appropriate reaction for the user to take in real time have been implemented [18]. Tanaka et al. (2008) designed "Let s Face It! Skills Battery (LFI! Battery)", a computer -based assessment, organized into a theoretical hierarchy of face processing domains that reinforce the child's ability to attend to faces, recognize facial identity and emotional expressions and interpret facial cues within a social context. The LFI! Battery was tested on participants with ASD and typically developing control (TDC) participants that were matched for age and IQ. Findings show that participants with ASD were able to label the basic facial emotions (with the exception of angry expression) on par with age-and IQmatched typically developing participants. However, participants with ASD were impaired in their ability to generalize facial emotions across different identities and a tendency to recognize the mouth feature holistically and the eyes as isolated parts. The results also indicate that a relatively short-term intervention program can produce measurable improvements in the face recognition skills of children with autism [19], [20].  presented "Mind Reading", an interactive multimedia program developed to teach adults with Asperger syndrome and high-functioning autism about emotions and mental states. It is based on a taxonomic system of 412 emotions and mental states, clustered into 24 emotion groups, and six developmental levels from four years old to adulthood. Mind Reading uses video, audio and written text to systematically introduce and teach basic and complex emotions. Users were able to explore emotions in the emotion library, partake in lessons and quizzes in the learning center and play games about emotions in the game zone. Results showed that following 10-20 hours of using the software over a period of 10-15 weeks, users significantly improved their ability to recognize complex emotions and mental states from both faces and voices, when compared to their performance before the intervention and compared with a control group [21]. LaCava et al. (2007) extended the work done by  by evaluating the ef"cacy of Mind Reading with a sample of eight American children with ASD age eight to eleven. Using a pre-test/post-test design, this study found that children improved on their ability to identify emotion from faces, after exposure to the multimedia training program. Additionally, participants were able to generalize emotion recognition skills to voices, but not faces that were not directly incorporated into the training program. Children, parents, and teachers indicated satisfaction with the format and content of the program and found it to be a highly motivating educational tool. Nonetheless, there was not strong support for the transfer of these skills to emotion recognition to more realistic and meaningful contexts [22].
Beumont and Sofronoff (2008) developed "The Junior Detective Training Program" an intervention program that included a computer game, small group sessions, parent training sessions and teacher handouts to teach social skills and emotional understanding to children with Asperger syndrome. The computer game was developed such PAPER EMPATHY, SPECIAL EDUCATION AND ICTS that the user was a detective who specialized in decoding other's mental and emotional states. Both human and computer-animated characters were utilized to teach emotion recognition and social problem solving. Support and mission outcomes were individualized and varied depending on how a user completed a given task. The study was tested on 49 children with Asperger syndrome between 7.5 and 11 years of age. Overall, "ndings from this study suggest that the Junior Detective Training Program may be an effective tool for teaching social functioning and emotion recognition to children with Asperger syndrome. However, although components of the intervention were developed speci"cally to enhance skill generalization, this study did not measure the generalization of targeted skills to real life social contexts [23]. Cheng et al. (2010) introduced a "Collaborative Virtual Learning Environment (CVLE) -empathy system" to promote the understanding of empathy for children with ASCs. The virtual environment is a restaurant presented to participants on a laptop. The CVLE-empathy system allows children to choose expressive avatars that represent themselves and through novel social events highlighting empathic situations children with ASCs practice empathy in a social context and express their emotional states to other users. The study was tested on three participants, lasted five months and the results showed that the CVLEempathy system had positive effects on the development of empathy and generalized this understanding of empathy to their daily lives [24]. Baron -Cohen et al. (2001) developed the 'Reading the Mind in the Eyes' Test in 1997 but there was a revised version in 2001. It requires the participant to identify the mental state of the person in the photo just from information around the eye region of the face. This instrument measures both emotion recognition, cognitive empathy and mental state. Each correctly answered item is awarded one point and each incorrectly answered or unanswered item is scored as zero. The "nal score is sum of all acquired points. The Eyes test, originally developed as a sensitive measure of subtle cognitive de"cits in individuals with autism spectrum conditions (ASC), correlates with scores on the self-report Empathy Quotient (EQ) in the general population. Results showed that individuals with AS or HFA, despite having at least normal intelligence, have speci"c difficulties in identifying subtle mental states and cognitive empathy [25].  presented "The Cambridge Mindreading Face-Voice Battery" for adults (assessed the recognition of 20 emotional concepts from facial expression video clips and speech segment audio clips taken from Mind Reading. After each clip was shown, participants were asked to choose which of four emotion words best described how the person in the clip was feeling). The CAM-C consists of two subtests-Face ER (Emotion Recognition) and Voice ER-each involving 45 questions, for a total of 90 items. The battery was given to males and females with AS and matched controls. Results showed that individuals with Asperger Syndrome (AS), when compared to control group, had more dif"culties in recognizing mental states and emotions from both faces and voices [26]. FaceSay uses an interactive approach with computer animated avatars, both humans and animals, to create a more life-like software program to teach face and emotion recognition skills. After observing the computer intervention sessions, children completed post -test measures. The children with LFA (low-functioning autism) demonstrated improvements in two areas of the intervention: emotion recognition and social interactions. The children with HFA (high functioning autism) demonstrated improvements in all three areas: facial recognition, emotion recognition, and social interactions [27].
Schulte-Rüther et al. (2011) examined subjects with ASD in an explicit empathizing task. Subjects were asked to empathize with emotional facial expressions presented on a computer screen by "feeling into" the depicted person and either to judge the emotional state of each face (othertask), or to report the emotions elicited in themselves by the emotional faces (self-task). Furthermore all participants completed the Autism Spectrum Questionnaire (AQ) and the Empathy Questionnaire (EQ). ASD subjects performed almost equally to the control group during the other-task, but showed less emotionally congruent responses in the self-task [28].

D. Attention deficit hyperactivity disorder (ADHD) and
Attention deficit disorder (ADD) learners Braaten and Rosen (2000) presented different stories to 6-12 years old patients with ADHD and healthy controls. Participant were instructed to answer questions about the feelings of the protagonist, their own feelings and the reason for their feelings. ADHD patients less frequently matched the emotion they identified in the protagonist with the one identified in themselves and gave fewer protagonist -centered interpretations [29]. Yuill and Lyon (2007) investigated children with ADHD by presented them with emotional and non -emotional situations. They were instructed to match emotional facial expressions to the protagonist (emotional task) and to match photographs (non -emotional task). Children with ADHD performed more poorly than the control group in the emotional task. However, they also show impairments when they had to make judgements about non -emotional characteristics of faces suggesting that deficits do not reflect specific social cognition impairments per se [30]. Marton et al. (2009) examined empathy and social perspective taking in 8 to 12 year old children with and without Attention -Deficit/ Hyperactivity Disorder (ADHD). For this study well -established questionnaires and scales were used as tools to measure empathy and social perspective in order to export results. The "Index of Empathy for Children and Adolescents" and the "Interpersonal Negotiation Strategies" are measures used to assess empathy and its relation to social development. The study has been tested to 92 children, 50 with a diagnosis of ADHD and 42 typically developing comparison children. As far as empathy is concerned the results indicated that children with ADHD were less empathic than comparison children but with no significant difference and furthermore that greater deficits in empathy are associated with oppositional and conduct problems rather than ADHD [31]. Kam et al. (2004) examined the effectiveness of the PATHS curriculum (Promoting Alternative THinking PAPER EMPATHY, SPECIAL EDUCATION AND ICTS Strategies) on the adjustment of school-age children with special needs. The PATHS preventive intervention program is based on the ABCD (affective-behavioralcognitive-dynamic) model of development. PATHS focuses on building emotional awareness and emotional regulation skills in early school years and integrating the skills in self -control with social problem-solving skills. PATHS takes a classroom-wide approach, and the curriculum is intertwined with the existing curriculum in the school. It also promotes a change in classroom atmosphere to increase empathy and openness in dealing with emotional needs. PATHS includes many activities to achieve the aims mentioned above. The curriculum was tested on 133 students studying on elementary schools and the results indicated that long term implementation of PATHS has positive effects but more research on bigger samples has to be done [32]. Spackman et al. (2005) examined the emotion recognition of children with language impairment by using facial expressions on photographs and music by listening to experts and indicated what emotion was being expressed. The study was tested on 43 children with learning impairments and 43 typically developing, age-matched peers, sampled from the age ranges of 5 to 8 and 9 to 12 years. The results showed that there was no difference at identifying happiness, anger, sadness and fear but children with learning impairments showed some difficulties from typical children in surprise and disgust. Furthermore results indicated differences from typical children in identifying the emotion expressed in music excerpts [33]. Bloom et al. (2009) examined abilities of adolescents to recognize, express, and understand facial expressions of emotion. The study was tested on 69 participants which were divided into three groups: 23 adolescents with NVLD (nonverbal learning difficulties), 23 matched adolescents without learning disabilities, and a comparable group of 23 adolescents with GLD (general learning difficulties). Relative scales were used including Wechsler Intelligence Scale for Children-Third Edition, Recognition measure: Pictures of Facial Affect (PFA), Expression measure and Understanding measure. It was discovered that adolescents with GLD were significantly worse at recognizing facial expressions of emotions than adolescents with NVLD and without LD, with no difference between the NVLD and NLD groups [34]. González et al. (2006) developed Proyecto Aprender, an accessible educational online resource targeting children with special educational needs. The overall objective is to build up and develop the physical, emotional, cognitive and communicational skills of learners with special educational needs, using new information and communications technologies to promote, to the greatest possible extent, their personal autonomy and social integration. For this purpose the project includes several multimedia activities for the learning objects and some of them has to do with the emotions and the development of empathy [35], [36].

III. CONCLUSIONS
Empathy is without question an important ability. It allows us to tune into how someone else is feeling, or what they might be thinking. Empathy allows us to understand the intentions of others, predict their behavior, and experience an emotion triggered by their emotion. In short, empathy allows us to interact effectively in the social world.
The articles reviewed above discussed the application of innovative computer technology to assessment, intervention and cultivation of empathy. Programs utilized interactive multimedia to introduce individuals with SEN to social communication concepts such as empathy, emotion recognition and social skills. There has been conducted great progress in the design and the development of ICT software programs to provide students with special educational needs the opportunity and the capability to develop empathy.
The development of future interactive multimedia programs should focus on ways to assess and enhance this capacity. Further research is needed not only to evaluate and explore empathy in children and adults with special educational needs but also to determine intervention methods that will help these children enhance both their cognitive ability and their understanding of various expressions of emotion (cognitive and affective empathy). This paper shows that computers may also be able to serve as tools that help improve affective communication for all.