A Co-citation Network of Young Children's Learning with Technology

Introduction With the fast-developing technologies and prevalent technology use in everyday life, the issue of young children's learning with technology (YCLT) has become increasingly important (Plowman, Stephen, & McPake, 2010). The growing trend of YCLT has attracted researchers' attention to the need to review the contents of the YCLT literature. They have confirmed the effects of technology on various domains of young children's learning. For example, Blok, Oostdam, Otter, and Overmaat (2002) performed a meta-analysis to review 42 research articles regarding computer-assisted instruction in support of beginning reading instruction. These researchers found that computer-assisted instruction was generally effective in terms of facilitating children's beginning reading abilities. Lankshear and Knobel (2003) developed a four-quadrant framework to categorize 31 studies that adopted new technologies to support early childhood oral language and literacy development. In their work, most of the studies were assigned to the quadrant that used stand-alone technological devices to facilitate children's encoding and decoding skills. Yelland (2005) synthesized the literature on the use of computers in early childhood education from 1994 to 2004. Based on a qualitative review, she indicated that the use of computers is helpful for promoting young children's language, cognitive, and social development. Slavin and his colleagues (2009) presented an alternative meta-analytic review, called best-evidence synthesis, to investigate the effectiveness of reading programs for elementary students. In their review, technology-enhanced reading was found to be correlated with reading achievement. Recently, Hsin, Li, and Tsai (2014) conducted a content analysis to investigate 87 empirical studies of young children's use of technology for learning. They concluded three factors influencing children's learning through technology, and identified "children's development of digital literacy" as the emerging trend. Despite the abundant information obtained from Hsin et al. (2014) and the other abovementioned reviews, how YCLT studies relate to each other is still unknown. Moreover, some important issues need to be explored: What are the most often cross-referenced research streams of YCLT? To what extent have these streams in the YCLT research network been extended by the follow-up papers? In addition, from the perspective of a large number of citers, what is the latent structure in the field of the main YCLT literature? To answer the above research questions, this study presents a structural overview of the current YCLT research based on the analysis of large citation data. Two complementary methods were used to achieve the above purposes: document co-citation analysis (DCA) and social network analysis (SNA). First, co-citation analysis, known as a powerful computational analysis from the field of bibliometrics, is used to detect the most frequently referenced topics underlying the literature structure (Small, 1973). A highly co-cited pair indicates that the nature of these two research papers is highly correlated; thus, follow-up studies are most likely to cite them together. All these citing and cited papers together form an invisible research network around the same issue (Small, 1973). Therefore, with the use of DCA, researchers can identify the core research issues within YCLT research based on the bibliographic evidence. Next, embedded in graph layout, SNA is advantageous for profiling the complex co-citation matrix which is obtained from co-citation ties (or research pairs) of the literature network (Borgatti, Everette, & Freeman, 2002). Moreover, based on the measures of centrality, SNA can identify the most central papers within the focal network. For example, some critical bridging papers that connect different research streams can be identified with the measure of betweenness centrality (Freeman, 1979). In sum, the current study goes beyond Hsin et al. …