National Taipei University of Education, Taiwan
<ychien<\/span>
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mail.ntue.edu.tw<\/span>><\/p>\nAbstract<\/h3>\n
This study is a lexical analysis of spoken discourse and text supporting vocabulary development for EFL (English as a foreign language) students who watch gameplay videos, with corroborating evidence that the vocabulary of two EFL learners was indeed enriched in an analysis of their spoken discourse while engaged in a gameplay task. The study first addresses whether the popular video game Minecraft provides the much needed diverse context and situated learning for EFL learners to engage in conversations. It next examines the vocabulary encountered by players in Minecraft itself to demonstrate the potential vocabulary coverage that learners would be exposed to while playing Minecraft. And finally, it analyzes the spoken language produced by a pair of L2 learners to corroborate that their vocabulary coverage during gameplay recapitulated the language found in these videos and in the game itself, to an extent greater than would be expected of their peers.<\/p>\n
The study shows that L2 learners need to know 4000-6000 word families to understand 95% of the words in Minecraft gameplay videos. The Minecraft blocks and items vocabulary contains 60% of 3k words and 30% of 4k-14k words from the Brown National Corpus, whereas 9% of the words encountered in Minecraft are not found in the 14k BNC list. These are words that may be common to native speakers of English but that L2 learners are not likely to encounter outside of class. In this case, learners may increase the breadth of their vocabulary knowledge more easily by being exposed to these less frequent words from watching Minecraft videos and playing the game. Finally, the results from analyzing second language learner Minecraft gameplay show that 95% of the vocabulary, including proper names and marginal words, are from the 2,000 most frequent word lists and 5% were from the 3k-14k word lists. The study concludes that, for L2 learners of English, Minecraft YouTube videos can serve as authentic language input sources with rich vocabulary coverage.<\/p>\n
Key Words: Minecraft, YouTube, frequency of occurrence, lexical analysis, gamification, games-based learning, video games, language learning<\/p>\n\n\n
Introduction<\/h3>\n\n\n\n
According to the official Minecraft wiki (https:\/\/minecraft.gamepedia.com\/Minecraft_Wiki<\/a>) and Valentin (2019), Minecraft has sold more than 176 million copies worldwide across different platforms. It has become so popular amongst kids that educators have begun to explore its possibilities for educational purposes. Many researchers believe that games are appropriate to and hold substantial potential for language learning (Kuhn, 2017, Young et al., 2012). Specifically, there has been a steady increase in the claims valuing the use of network-based gaming for language learning. Peterson (2010) suggested that learner participation in network-based gaming provides valuable opportunities for vocabulary acquisition and also the context to develop communicative competence while interacting with online players otherwise not available in countries where English is a foreign language.<\/p>\n\n\n\n In the TEFL field, many parents of young EFL (English as a\nforeign language) learners who play Minecraft have reported observing language\nacquired through watching YouTube videos. According to the lead author in\nSmol\u010dec, Smol\u010dec, and Stevens (2014): <\/p>\n\n\n\n However, there has been little research to support such\nclaims. Therefore, this study seeks to provide a vocabulary coverage analysis\nto explore the vocabulary learning potential of learners who play Minecraft and\nwatch Minecraft gameplay videos.<\/p>\n\n\n\n Minecraft YouTube videos provide authentic language input, arguably even more so than television programs for young learners. Many research studies have focused on using television programs as a source of authentic language input and argued that these programs, although scripted, are still identified as authentic language because the target audiences of such programs are native speakers of the language who are \u201cauthentically representative of the input English speakers regularly come into contact with\u201d (Rodgers and Webb, 2011, p.690). Furthermore, Minecraft YouTube channels mostly contain people talking about what they are doing while streaming gameplay, which means they comprise spontaneous and naturally occurring monologues and dialogues. Therefore, they would be considered to contain authentic language, language used by players while they engage in gameplay. There have been studies of the language used in multiplayer online games; however, to my knowledge, none has focused on examining Minecraft, which is effectively a sandbox where players often use voice or text to converse authentically in order to decide what to build on their own. <\/p>\n\n\n\n Gaming\nvideos appear to be conducive to English vocabulary learning. Peters\u2019\nstudy (2019) on the effect of imagery and on-screen text on foreign language\nvocabulary learning from audio-visual input shows that words which accompany\non-screen imagery are three times more likely to be learned incidentally than\nwords without imagery. Peters also found that students performed higher meaning\nrecall and form recognition of words as a result of vocabulary learning under\naudio-visual input with words and on-screen imagery. In Rogers\u2019 study (2018)\nwords that were recalled by students most frequently were words with on-screen\nimagery or with clear visual support; for example, words and phrases such as\n\u2018pack of wolves\u2019, \u2018cubs\u2019, or \u2018den\u2019. Rogers also found that, with visual\nsupport, there was conceptual learning of words his students had not known\nprior to his study. Gee (2010) explained that games provide learners a situated context\nwhere they can learn vocabulary by experiencing and interacting in the\nvirtual environment as opposed to learning words by getting definitions\ncontaining even more words. In his view, the gaming environment provides the\nvisual meaning to visualize and thus better understand the words. <\/p>\n\n\n\n Understanding how Minecraft\ngamers communicate and use specific language is valuable for language\neducators, particularly with respect to game-based learning as a pedagogical\napproach (Bawa, 2018). Coyle (2010) proposed the language triptych in raising\nawareness of three types of language needed for a successful CLIL lesson. This\ntriptych comprises the three notions of language — of, for, and through —\nlearning. In Coyle\u2019s view, language of <\/em>learning\nfocuses on language related to understanding the subject; language for <\/em>learning includes functional\nlanguage for carrying out language tasks; language through <\/em>learning is the new language acquired because of the\nprocess. By adapting the language triptych, the language needed in playing\nmassively multiplayer online games can also be seen to include three aspects:\nlanguage of <\/em>gaming, language for <\/em>gaming, and language through <\/em>gaming, as explained below. <\/p>\n\n\n\n Language of gaming<\/strong> is the language involved in the virtual world in-game. Minecraft (Mojang, 2015) is a multiplayer sandbox building game which, in the beginning, was simply a game about breaking and placing blocks. However, its functionality became more complex with the introduction of different types of blocks and materials such as redstone to transport signals and create complex functionality such as automated circuit devices to make the gameplay tasks easier. Therefore, the language of gaming includes the names of the blocks, items, and actions that can be done during gameplay in the virtual world, such as \u2018crafting\u2019, \u2018smelting\u2019, \u2018enchanting\u2019, \u2018brewing\u2019, and \u2018trading\u2019. There are also different types of mobs, or moving entities in-game. There are passive mobs such as animals; hostile mobs such as \u2018creepers\u2019, \u2018ghasts\u2019, and \u2018zombies\u2019; and tamable mobs such as \u2018cats\u2019, \u2018donkeys\u2019, \u2018llamas\u2019, \u2018mules\u2019, \u2018parrots\u2019, and \u2018wolves\u2019. <\/p>\n\n\n\n An additional affordance of Minecraft in terms of the vocabulary players are exposed to is the variety of biomes that can be used as ecological representations. Minecraft provides a variety of ecological systems with different forms of plants and animals, such as the desert biome, the forest biome, and the recently introduced ocean biome, with its own unique set of oceanic creatures. Each ecosystem has its unique set of vocabulary particular to that domain. <\/strong><\/p>\n\n\n\n Language for gaming<\/strong> comprises the language functions needed\nto interact with online players and perform game quests or tasks. Parents and\nresearchers have reported that players appear to be endlessly engaged in\ncommunication and cooperation during gameplay in Minecraft. Players may play on\nMinecraft servers either with their neighbors or with other players around the\nworld. They may work together and pool resources, build structures, defeat\nhostile mobs, and\/or trade tips pertaining to gameplay; thereby fostering\nsocial skills in communication. Furthermore, Minecraft is a virtual world that\nrelies on its players\u2019 creativity and problem-solving skills; thus, it\u2019s a\nvirtual world that elicits from learners the language needed for\nproblem-solving, creativity, and collaboration. Collaborative language is\nmostly modeled by other players in the game. By listening to other players\nonline, the learners are able to acquire the language they need for gaming. <\/p>\n\n\n\n Language through gaming<\/strong> is the new language learned in the\nprocess of playing. This aspect of language learning is almost unpredictable\nand without boundaries. Given that Minecraft is a multiplayer online gaming\nplatform, while players engage in playing with people from different parts of\nthe world, cross-cultural communication enriches their language repertoire and\nnew target language and culture are learned in the process of playing. One\nexample from my own data is where the author\u2019s son, Mattie, did not understand\nwhy Jacob in England was going to have tea during dinner time and later found\nout tea is their way of saying dinner time. Another example occurred during a\ndiscussion of llamas and alpacas in Minecraft. In this instance, the kids were\nnot only acquiring new vocabulary but also learning new concepts such as the\ndifferences between llamas and alpacas.<\/p>\n\n\n\n In Zheng, Bischoff, and\nGilliland\u2019s (2015) investigation of a Japanese learner, Conan, and his\nvocabulary learning in the massively multiplayer online game (MMOG) World of\nWarcraft, the researchers observed that Conan had actively taken advantage of\nhis co-player by initiating questioning about the words \u2018repop\u2019 and \u2018looting\u2019.\nThe researcher argued that the sequence of vocabulary learning in MMOG reflects\nthe participatory, collaborative, and distributed nature of learning, providing\nthe learners with the ability to simultaneously see and do in the course of\ngameplay in an enriching context through which to acquire the vocabulary. In\nthis way, language is learned through gaming and interacting with online\nplayers in the virtual world; thus learners are engaged in first-order languaging<\/em>, \u201ca whole-body sense-making\nactivity that enables persons to engage with each other in forms of coaction\nand to integrate themselves with and to take part in social activities\u201d\n(Thibault, 2011, p. 215), and not just learning\nabout language<\/em>, which is a second-order construct.<\/p>\n\n\n\n Understanding the vocabulary coverage in\nMinecraft videos would help debunk the notion that watching Minecraft streaming\nvideos is just a waste of time (Vrabel, 2017) but, on the contrary, would in\nfact be beneficial for language learning. There has been\nlittle research in investigating lexical coverage of the language used by\nplayers in gaming videos and\nnone so far has determined the lexical coverage necessary for adequate\ncomprehension of YouTube Minecraft gameplay videos. Therefore, studies\nof lexical coverage and reading comprehension, which have been more extensively\nstudied, may shed some light on how much vocabulary is needed to comprehend\nMinecraft gaming videos for L2 learners. <\/p>\n\n\n\n Nation (2006) examined the coverage of a variety of spoken and written texts with the 14 frequency lists developed on the basis of the British National Corpus (BNC) and the data show that in written texts, the first thousand most frequent word families provide a coverage of 81%, the second thousand an additional 9%, and the third thousand 5%. Thus, these data show that readers with a knowledge of 3,000 word families can reach a coverage of 95% of the words used in the BNC. Laufer and Ravenhorst-Kalovshki (2010) revisited the lexical threshold for reading comprehension and suggested that if corpus analysis showed that 8,000 word families cover 98% of a text, and if learners obtained a score of 71% in a reading comprehension test when they understood 98% of a text, then that level of adequate reading comprehension would require the knowledge of 8,000 word families. Therefore, Laufer and Ravenhorst-Kalovshki proposed two thresholds: the knowledge of 8,000-word families yielding the coverage of 98% would be an optimal threshold and minimal comprehension would require a vocabulary size of 4,000\u20135,000 word families resulting in the coverage of 95%. <\/p>\n\n\n\n Since these figures were based on the results of text comprehension, we might project that for comprehending gaming videos, assuming that the learners might be able to understand more with visuals in the videos, an estimate of 4,000 word families with 95% coverage may be sufficient for learners to comprehend the Minecraft YouTube videos. <\/p>\n\n\n\n Meanwhile, Adolphs and Schmitt (2003) conducted a study on lexical coverage of spoken discourse to investigate the number of words that are required to speak conversationally in the L2. The authors indicated that the common consensus that learners are to acquire 2,000 word families to be able to engage in daily conversation is based on a study by Schonell et al. (1956; as cited in Adolphs & Schmitt, 2003) of a corpus of spoken discourse that consisted of only 512,647 words collected from Australian workers at the time. Since much larger and more diverse spoken corpora are available, e.g. the Cambridge and Nottingham Corpus of Discourse in English, Adolphs and Schmit analyzed the CANCODE, which contains 5 million words, and found that 2,000 word families made up less than 95 percent coverage, and that the most frequent 3,000 word families covered nearly 96 percent of the spoken corpus.<\/strong> Their study, which performed a second analysis on the CANCODE, and the spoken component of the British National Corpus (BNC), shows that approximately 5,000 words were required to achieve about a 96 percent coverage figure. <\/p>\n\n\n\n This result was an indication that more vocabulary is necessary to engage in daily spoken discourse than was previously thought. Therefore, performing an analysis of spoken discourse in Minecraft YouTube videos would allow us to know whether Minecraft gameplay videos reflect the daily spoken discourse, as suggested in the results found in Adolph and Schmitt\u2019s study, to determine whether these videos would serve as sufficient language input for L2 learners. <\/p>\n\n\n\n The present study focuses on the vocabulary\ncoverage of Minecraft YouTube gaming channels in order to gauge the vocabulary\nlearning potential for L2 learners when they watch the videos. The purposes are\nthree-fold: 1) to investigate the lexical coverage of Minecraft videos; 2) to\nanalyze the lexical coverage of Minecraft block and item names and thereby\nprovide information on the language of gaming; and 3) to analyze L2 learner\ngameplay vocabulary. <\/p>\n\n\n\n Based on the spoken corpus-based study by Adolphs and Schmitt (2003), 3,000 word families were determined to cover nearly 96% of the words that occurred in the spoken corpus, and drawing on text comprehension studies (Nation, 2006; Laufer and Ravenhorst-Kalovshki, 2010), it appears that vocabulary size needs to reach at least 95% of lexical coverage of the text for minimal comprehension. <\/p>\n\n\n\n So to address the first purpose of this research, this study seeks to investigate whether 3,000 word families in the BNC cover 96% of the Minecraft gameplay spoken corpus and what vocabulary size is necessary to reach 95% coverage of the Minecraft gameplay spoken corpus. Regarding the language of gaming, the study then analyzes the vocabulary frequency of the names of blocks and items. And finally, the study ends in analyzing a 10-minute building challenge gameplay of two young players and reports the vocabulary range of L2 learner gameplay, classifying the words by their frequency against the BNC wordlist. <\/p>\n\n\n\n For research purposes\none and two, the automated closed captions of 106 Minecraft videos were\nrandomly selected, downloaded, and analyzed in this study. The videos used came\nfrom the following YouTube channels and were selected based on the popularity\nand purpose of the channels: <\/p>\n\n\n\n PopularMMOs videos are\nnatural dialogues between two American players, Pat and Jenn, filmed while they\nuncover new challenges while playing Minecraft together. Sixty-one of the most popular videos\u2019 closed\ncaptions were downloaded from the PopularMMOs channel. For example, the most\nviewed video with 54 million views was Minecraft\nmore tnt mod (35 tnt explosives and dynamite!) too much tnt mod showcase<\/em>\n(Popularmmos, 2013) uploaded in 2013.\nThe video was 52 minutes long and contains 4176 words. <\/p>\n\n\n\n Twenty-five videos\nof The\nbest Minecraft Series Ever w\/DanTDM<\/em> (DanTDM, 2018) were used, and for\nOMGcraft (Johnson, 2012), twenty videos were selected. Both DanTDM and OMGcraft\nchannels are monologues of the hosts either playing or creating tutorials\ninforming listeners how to play. <\/p>\n\n\n\n All in all, the Minecraft video gameplay corpus consists of 464,605 words of automated transcribed closed captions from the YouTube videos, broken down as follows: Popularmmos, (61 videos, 226k words), DanTDM (25 videos, 205k words), and OMGcraft (20 videos, 32k words). DanTDM and PopularMMOs were selected based on the popularity of the channel, but OMGcraft was selected because of the clarity and pace of the speech. The OMGcraft channel host talks more slowly and more clearly than Pat and Jenn in Popularmmos, therefore OMGcraft may be more suitable for L2 learners. <\/p>\n\n\n\n For research purpose\nthree, a 10 minute audio recording of a conversation between two young L2\nlearners during a Minecraft building challenge was transcribed and used for\nanalysis. <\/p>\n\n\n\n The AntWordProfiler was\nused to analyze the transcripts. AntWordProfiler is a \u201cfreeware tool for\nprofiling the vocabulary level and complexity of texts\u201d\n(Anthony, 2014, n.p.) that lists the words that occur in a text according to their\nfrequency. Nation\u2019s (2006) BNC word family list of fourteen 1,000 word lists were\nused with the AntWordProfiler software to show the percentage lexical coverage\nof the 14 groups of 1,000 words at which the words in the Minecraft YouTube\nvideos occurred. The lists are based on the frequency and range of occurrence\nof words in the spoken section of the British National Corpus. <\/p>\n\n\n\n In addition, two other\nlists were used for analysis of the spoken corpus: a list of proper nouns and a\nlist of marginal words containing four headwords which include mostly\ninterjections, exclamations, and hesitation procedures, all of which are common\nin spoken English. Items that are not listed in the most frequent 14,000\nword-families may be classified as \u2018Not in the lists\u2019. The BNC word lists can\nbe downloaded from Paul Nation\u2019s website: https:\/\/www.victoria.ac.nz\/lals\/resources\/range<\/a><\/p>\n\n\n\n To address research purpose one regarding the vocabulary learning potential for L2 learners when watching gameplay videos, a corpus consisting of 464,605 words from three Minecraft YouTube channels was used for analysis against the BNC 14K word lists. More specifically, this study examined whether 3,000 word families would cover 96% of the Minecraft gameplay spoken corpus and what vocabulary size is necessary to reach 95% coverage. <\/p>\n\n\n\n Table 1 shows the cumulative coverage, in percent, of the complete corpus of dialog from the Minecraft gameplay videos arrayed against the 14k most frequent words in the BNC corpus. In the second column the percent coverage is shown for the three channels combined, and then each is shown separately in the last three columns. As can be seen in the totals at the bottom of the table, the complete spoken corpus of gameplay videos consisted of 464,605 tokens. The last two rows of the table show the total numbers of word types and word families found for each of the columns; e.g the combined channels consisted of 10,359 word types from 6,977 word families. <\/p>\n\n\n\n The results in Table 1 show that a vocabulary size of 3,000 word families provided only 94% coverage of all videos in the three different channels combined. For all three channels, 88.11% of the vocabulary used in the videos make up the first thousand words, and 92.37% of that vocabulary were from the first 2k words. In order to reach 95% of coverage, learners would need to have a vocabulary size of 5,000 word families across three channels. As indicated by the figures in bold in the table below, the vocabulary threshold is lower in DanTDM\u2019s videos, with 4,000 word families covering 95.42% of the vocabulary used in the videos and in OMGcraft\u2019s videos, with 4,000 word families covering 95.06%; whereas with Populammos, viewers would need to be familiar with 6,000 word families in order to achieve that level of coverage. <\/p>\n\n\n Table 1I learned how Filip had acquired a\nhigh level of English through watching YouTube videos, mainly ones about\nMinecraft, and interacting with players from other countries, often to exchange\nexpertise on the game, but more recently in making his own videos of gameplay\nand tutorials to explain his techniques to others. (p. 6)<\/blockquote><\/p>\n\n\n\n
Minecraft Videos Provide Authentic Language Input<\/h4>\n\n\n\n
The Language Triptych for Gaming<\/h4>\n\n\n\n
Lexical Demand for Comprehension<\/h4>\n\n\n\n
Purpose of the Study<\/h3>\n\n\n\n
Method<\/h3>\n\n\n\n
Materials<\/h4>\n\n\n\n
Data Analysis <\/h4>\n\n\n\n
Results and Discussion<\/h3>\n\n\n\n
Lexical Coverage of Minecraft Videos<\/h4>\n\n\n\n
\nCumulative Coverage for Three Minecraft YouTube Channels<\/em><\/p>\n
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