Journal articles: 'Learning texts'

1

Garner, Ruth. "Learning From School Texts." Educational Psychologist 27, no. 1 (January 1992): 53–63. http://dx.doi.org/10.1207/s15326985ep2701_5.

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Hall-Kenyon, Kendra M., and Sharon Black. "Learning From Expository Texts." Topics in Language Disorders 30, no. 4 (2010): 339–49. http://dx.doi.org/10.1097/tld.0b013e3181ff21ea.

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Hollis, Karen L. "Animal Learning Texts: "Learning and Something Else"." Contemporary Psychology: A Journal of Reviews 32, no. 1 (January 1987): 24–25. http://dx.doi.org/10.1037/026650.

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Pistol, Ionuț, Diana Trandabăț, and Mădălina Răschip. "Medi-Test: Generating Tests from Medical Reference Texts." Data 3, no. 4 (December 19, 2018): 70. http://dx.doi.org/10.3390/data3040070.

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The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using medical reference texts as input and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false, and match), can have customizable length and difficulty, and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.

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Beck, Isabel L., Margaret G. McKeown, and Erika W. Gromoll. "Learning From Social Studies Texts." Cognition and Instruction 6, no. 2 (June 1989): 99–158. http://dx.doi.org/10.1207/s1532690xci0602_1.

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Cheng, Chin-Chuan. "Learning words with many texts." ACM SIGCUE Outlook 26, no. 3 (July 1998): 16–27. http://dx.doi.org/10.1145/311110.311114.

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McNamara, Danielle S., and Walter Kintsch. "Learning from texts: Effects of prior knowledge and text coherence." Discourse Processes 22, no. 3 (October 1996): 247–88. http://dx.doi.org/10.1080/01638539609544975.

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Clinton, Virginia, and Paul van den Broek. "Interest, inferences, and learning from texts." Learning and Individual Differences 22, no. 6 (December 2012): 650–63. http://dx.doi.org/10.1016/j.lindif.2012.07.004.

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Leland, Christine, Anne Ociepka, Kate Kuonen, and Sara Bangert. "Learning to Talk Back to Texts." Journal of Adolescent & Adult Literacy 61, no. 6 (January 11, 2018): 643–52. http://dx.doi.org/10.1002/jaal.730.

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Kelly, Caitlin. "Learning to Talk Back to Texts." Pedagogy 18, no. 1 (January 1, 2018): 174–80. http://dx.doi.org/10.1215/15314200-4217026.

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Dole, Janice A. "READERS, TEXTS AND CONCEPTUAL CHANGE LEARNING." Reading & Writing Quarterly 16, no. 2 (April 2000): 99–118. http://dx.doi.org/10.1080/105735600277980.

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Shamsfard, Mehrnoush, and Ahmad Abdollahzadeh Barforoush. "Learning ontologies from natural language texts." International Journal of Human-Computer Studies 60, no. 1 (January 2004): 17–63. http://dx.doi.org/10.1016/j.ijhcs.2003.08.001.

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13

Xia, Jianping. "Multidimensional Electronic Texts Benefits Literacy Learning." Science Insights Education Frontiers 8, S1 (January 22, 2021): 8. http://dx.doi.org/10.15354/sief.21.s1.ab019.

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Chinese class is an essential subject in humanities and social science, with the characteristics of instrumentality and humanity, and the concentration on students’ language construction, cultural understanding, thinking development, and aesthetic appreciation. Meanwhile, the development of Chinese language literacy must be based on words cognitive and language using abilities. The application of multidimensional electronic texts is helpful to Chinese literacy teaching and the development of students’ wisdom. Specifically, the multidimensional electronic text contains assorted new words and a series of words and paragraphs, including group study requirements, reading, dubbing, paragraph generalization, summary, central ideas, writing features, word assessment, etc. This article analyzed specific supporting functions of multidimensional electronic texts in teaching Chinese character “literacy”; It analyzed and showed the specific application methods of multidimensional electronic texts in primary school literacy teaching roles, promoting students’ wisdom development by selecting specific cases.

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Rokhatoy, Abidova, and Madraimov Jafar. "The Role Of Literary Texts In Foreign Language Learning For ESP Classess." American Journal of Social Science and Education Innovations 02, no. 11 (November 30, 2020): 537–42. http://dx.doi.org/10.37547/tajssei/volume02issue11-92.

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This article discusses a novel method of using modern fiction literary texts selected according to the specifics of teaching, contains examples of various speech genres reflecting the state of the language today, and gives an idea of how communication in English takes place in various areas of communication, including professional communication in language learning process. The professional vocabulary is manifested both in the professional communication of the characters in the work and in the description of everyday situations.

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Gülgöz, Sami, G. Tarcan Kumkale, M. Emrah Aktunç, and T. Terry Eskenazi. "Designing learnable texts." Document Design 1, no. 2 (December 31, 1999): 103–14. http://dx.doi.org/10.1075/dd.1.2.04gul.

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Revising texts to improve learning has produced successful revisions but the reasons why they were successful has not been made clear. In this review, we describe the text revision strategy derived from the text processing model of Kintsch by Britton and Gülgöz (1991). Three principles were developed and implemented on natural texts to make them more memorable. The texts used in different experiments varied in content and language and the studies employed different measures of learning and reading time. The emerging pattern from the reviewed studies is that revised texts that add the missing connections to the text, lead to better learning, especially when readers lack inference-making skills, domain-specific knowledge or motivation for cognitive effort.

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Beker, Katinka, Dietsje Jolles, Robert F. Lorch, and Paul van den Broek. "Learning from texts: activation of information from previous texts during reading." Reading and Writing 29, no. 6 (February 3, 2016): 1161–78. http://dx.doi.org/10.1007/s11145-016-9630-3.

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Wolfe, Michael B. W., M. E. Schreiner, Bob Rehder, Darrell Laham, Peter W. Foltz, Walter Kintsch, and Thomas K. Landauer. "Learning from text: Matching readers and texts by latent semantic analysis." Discourse Processes 25, no. 2-3 (January 1998): 309–36. http://dx.doi.org/10.1080/01638539809545030.

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Ukrainetz, Teresa A. "Strategic Intervention for Expository Texts: Teaching Text Preview and Lookback." Perspectives of the ASHA Special Interest Groups 1, no. 1 (March 31, 2016): 99–108. http://dx.doi.org/10.1044/persp1.sig1.99.

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Students must understand, learn from, and compose diverse genres of oral and written expository discourse for many purposes. From this broad domain, speech-language pathologists (SLPs) need to make strategic choices that will result in their students becoming more independent, capable learners. This article explains the important role of learning strategies in dealing with informational texts and how SLPs are suited to teach strategies to struggling readers. Specific attention is given to two simple strategies that can make noticeable differences in student learning: text preview and lookback.

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Subrahmanyam, Kaveri, Minas Michikyan, Christine Clemmons, Rogelio Carrillo, Yalda T. Uhls, and Patricia M. Greenfield. "Learning from Paper, Learning from Screens." International Journal of Cyber Behavior, Psychology and Learning 3, no. 4 (October 2013): 1–27. http://dx.doi.org/10.4018/ijcbpl.2013100101.

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Electronic screens on laptop and tablet computers are being used for reading text, often while multitasking. Two experimental studies with college students explored the effect of medium and opportunities to multitask on reading (Study 1) and report writing (Study 2). In Study 1, participants (N = 120) read an easy and difficult passage on paper, a laptop, or tablet, while either multitasking or not multitasking. Neither multitasking nor medium impacted reading comprehension, but those who multitasked took longer to read both passages, indicating loss of efficiency with multitasking. In Study 2, participants (N = 67) were asked to synthesize source material in multiple texts to write a one-page evidence-based report. Participants read the source texts either on (1) paper, (2) computer screen without Internet or printer access, or (3) computer screen with Internet and printer access (called the “real-world” condition). There were no differences in report quality or efficiency between those whose source materials were paper or computer. However, global report quality was significantly better when participants read source texts on a computer screen without Internet or printer access, compared with when they had Internet and printer access. Active use of paper for note-taking greatly reduced the negative impact of Internet and printer access in the real-world condition. Although participants expressed a preference for accessing information on paper, reading the texts on paper did not make a significant difference in report quality, compared with either of the two computer conditions. Implications for formal and informal learning are discussed.

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Kebede, Gedefaw Abie, and Yihenew Melese Milkitie. "A Panoramic Evaluation of Literary Texts in Preparatory Students’ English Text Books." Theory and Practice in Language Studies 8, no. 8 (August 1, 2018): 996. http://dx.doi.org/10.17507/tpls.0808.11.

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Learning language through literature has got a great emphasis in foreign language class room since the use of literary text as a resource for teaching macro and micro skills is very popular within the field of language learning. Literary texts increase the learners’ proficiency of integrated language learning as they enable the learners to apply all macro and micro skills. Of the issues to be considered, the contents, their levels of difficulty, their diversity and the appropriateness of the tasks designed based on the contents. This study evaluated the literary texts in preparatory students English text books. Based on the objectives of the study, both qualitative and quantitative research method was employed. The result of the study indicated that the most of the literary texts are above the language competence of the students and the teachers. Teachers strongly affirmed that they have faced to understand the literary texts’ language. The literary texts are not considering the students’ cultural background since almost all of the texts are written by foreign in a foreign context. Moreover, the majority of the literary texts are not related with the unit topic of the book. Using the literary materials in language class, therefore, should take into account the students’ need, motivation, interests, cultural background and language competence.

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Sato, Koichi, and Sea Saito. "Testing effects on learning of historical texts." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): 2A—058–2A—058. http://dx.doi.org/10.4992/pacjpa.81.0_2a-058.

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22

Todd, P. A. "Experience(s) in creating distance learning texts." Bioscience Education 10, no. 1 (December 2007): 1–6. http://dx.doi.org/10.3108/beej.10.3.

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23

Sharif, Omar, Mohammed Moshiul Hoque, A. S. M. Kayes, Raza Nowrozy, and Iqbal H. Sarker. "Detecting Suspicious Texts Using Machine Learning Techniques." Applied Sciences 10, no. 18 (September 18, 2020): 6527. http://dx.doi.org/10.3390/app10186527.

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Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents has been growing enormously in recent years through instant messaging, social networking posts, blogs, online portals and other digital platforms. Unfortunately, the misapplication of technologies has increased with this rapid growth of online content, which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio, or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier ‘tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

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24

Nicoll, Katherine, and Richard Edwards. "Reading policy texts: lifelong learning as metaphor." International Journal of Lifelong Education 19, no. 5 (September 2000): 459–69. http://dx.doi.org/10.1080/026013700445576.

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25

Hall, Kathy. "Challenging traditional representations of texts and learning." Literacy 42, no. 3 (October 24, 2008): 121–22. http://dx.doi.org/10.1111/j.1741-4369.2008.00500.x.

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Ghilay, Yaron. "Text-Based Video: The Effectiveness of Learning Math in Higher Education Through Videos and Texts." Journal of Education and Learning 10, no. 3 (April 13, 2021): 55. http://dx.doi.org/10.5539/jel.v10n3p55.

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The Text-Based Video (TBV) model is a particular case of the more general Video-Based Learning (VBL) model in which an instructor’s curriculum is fully covered by high-quality videos and texts. The aim of this study is to test the effectiveness of the TBV model by examining and comparing its two main components: Videos and texts. The model is based on the creation of high-quality texts which form the basis for high-quality video clips. It is designed to improve learning in quantitative courses in higher education. The research was based on a sample of students  who enrolled in the course Mathematics for Business Administration at the Neri Bloomfield School of Design and Education, Haifa, Israel that was based on the TBV model. The course was given during the five academic years 2016-2021 using different teaching formats: face-to-face learning, distance learning and blended learning. Learners were asked to answer an online questionnaire that assessed the characteristics and advantages/disadvantages of TBV. The findings show that although students preferred watching videos based on texts over reading those texts alone, students opined that the combination of video and text was by far the most effective instructional method. All results were identical regardless of whether face-to-face, distance or blended learning was used.

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Verma, Mayuri. "Lexical Analysis of Religious Texts using Text Mining and Machine Learning Tools." International Journal of Computer Applications 168, no. 8 (June 15, 2017): 39–45. http://dx.doi.org/10.5120/ijca2017914486.

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Schlag, Sabine, and Rolf Ploetzner. "Supporting learning from illustrated texts: conceptualizing and evaluating a learning strategy." Instructional Science 39, no. 6 (November 10, 2010): 921–37. http://dx.doi.org/10.1007/s11251-010-9160-3.

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Hootsen, Geke, Rintse van der Werf, and Anne Vermeer. "E-learning op Maat." In gesprek 78 (January 1, 2007): 119–30. http://dx.doi.org/10.1075/ttwia.78.11hoo.

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The present study explored the effect of reading texts in an electronic learning environment for 46 adult second language learners of Dutch with different proficiency levels. The reading materials were selected dynamically, based on the "fit" between the vocabulary proficiency of individual students and the relative difficulty of the texts. Text difficulty was determined on the basis of the relative proportion of words unknown to the learner (lemma coverage). The texts were analyzed, selected and presented online, accompanied by a personalized electronic dictionary with words that were assumed to be unknown to the reader. The vocabulary learned implicitly while reading was measured in a pretest - treatment - posttest design. The learners read three texts, with lemma coverages of 92%, 90% and 85%, respectively. The relation between dictionary use and word retention was also examined. The study revealed an average word-learning improvement of 32,5%, with no differences between texts with a 92%, 90% or 85% lemma coverage. With respect to dictionary use, the learners looked up an average of 8.0 words. Two-thirds of these words were known to them; they looked up only one-third of the unknown words. We found there was a relation between dictionary use and the retention of unknown words. We concluded that the method we propose for an automated adaptive selection of reading texts and individualized dictionaries ensures that learners of different proficiency levels receive linguistic input that is best suited to their abilities.

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Adesope, Olusola O., Andy Cavagnetto, Nathaniel J. Hunsu, Carlos Anguiano, and Joshua Lloyd. "Comparative Effects of Computer-Based Concept Maps, Refutational Texts, and Expository Texts on Science Learning." Journal of Educational Computing Research 55, no. 1 (July 26, 2016): 46–69. http://dx.doi.org/10.1177/0735633116654163.

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This study used a between-subjects experimental design to examine the effects of three different computer-based instructional strategies (concept map, refutation text, and expository scientific text) on science learning. Concept maps are node-link diagrams that show concepts as nodes and relationships among the concepts as labeled links. Refutational texts are designed specifically to elicit common misconceptions that learners typically hold about a particular topic, directly refute the misconceptions, and present scientific explanations as compelling alternatives. Expository scientific texts consist of texts that are written specifically to present correct scientific information. Sixty-seven participants were randomly assigned to study one of three computer-based presentations on climate change. The dependent measures were tests of free recall and transfer. The concept map group significantly outperformed the refutational and scientific text groups on both free recall and transfer tests. Practical and theoretical implications of these findings are discussed.

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Diakidoy, Irene-Anna N., Thalia Mouskounti, and Christos Ioannides. "Comprehension and Learning From Refutation and Expository Texts." Reading Research Quarterly 46, no. 1 (January 3, 2011): 22–38. http://dx.doi.org/10.1598/rrq.46.1.2.

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Zavitsanos, Elias, Georgios Paliouras, George A. Vouros, and Sergios Petridis. "Learning subsumption hierarchies of ontology concepts from texts." Web Intelligence and Agent Systems: An International Journal 8, no. 1 (2010): 37–51. http://dx.doi.org/10.3233/wia-2010-0178.

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Hamiti, Mentor, and Agni Dika. "Learning opportunities through generating speech from written texts." Procedia - Social and Behavioral Sciences 2, no. 2 (2010): 4319–24. http://dx.doi.org/10.1016/j.sbspro.2010.03.686.

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Thiede, Keith W., Mary C. M. Anderson, and David Therriault. "Accuracy of metacognitive monitoring affects learning of texts." Journal of Educational Psychology 95, no. 1 (2003): 66–73. http://dx.doi.org/10.1037/0022-0663.95.1.66.

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Liou, Hsien‐Chin. "THE IMPACT OF WWW TEXTS ON EFL LEARNING." Computer Assisted Language Learning 10, no. 5 (November 1997): 455–78. http://dx.doi.org/10.1080/0958822970100505.

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Iding, Marie K. "CAN QUESTIONS FACILITATE LEARNING FROM ILLUSTRATED SCIENCE TEXTS?" Reading Psychology 18, no. 1 (January 1997): 1–29. http://dx.doi.org/10.1080/0270271970180101.

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SATO, Kouichi, Kanako ASABUKI, Hideaki SUMIKURA, Makoto TSUNODA, and Yoshiro MORITA. "FUTURE LECTURES AND TEXTS FOR LEARNING BUILDING PRODUCTION." Journal of Architecture and Planning (Transactions of AIJ) 82, no. 733 (2017): 827–35. http://dx.doi.org/10.3130/aija.82.827.

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Hickman, Jarmila. "Literary texts and intercultural learning. Exploring new directions." Language and Intercultural Communication 13, no. 2 (May 2013): 249–51. http://dx.doi.org/10.1080/14708477.2013.771517.

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39

Schnotz, Wolfgang, Ulrich Ludewig, Mark Ullrich, Holger Horz, Nele McElvany, and Jürgen Baumert. "Strategy shifts during learning from texts and pictures." Journal of Educational Psychology 106, no. 4 (2014): 974–89. http://dx.doi.org/10.1037/a0037054.

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Punuru, Janardhana, and Jianhua Chen. "Learning non-taxonomical semantic relations from domain texts." Journal of Intelligent Information Systems 38, no. 1 (January 21, 2011): 191–207. http://dx.doi.org/10.1007/s10844-011-0149-4.

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Xu, Dongliang, Zhihong Tian, Rufeng Lai, Xiangtao Kong, Zhiyuan Tan, and Wei Shi. "Deep learning based emotion analysis of microblog texts." Information Fusion 64 (December 2020): 1–11. http://dx.doi.org/10.1016/j.inffus.2020.06.002.

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42

Lavergne, Thomas, Tanguy Urvoy, and François Yvon. "Filtering artificial texts with statistical machine learning techniques." Language Resources and Evaluation 45, no. 1 (January 16, 2010): 25–43. http://dx.doi.org/10.1007/s10579-009-9113-0.

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Ryabtseva, Irina Gennadievna, and Naile Makhir gizi Magamedli. "INTERNET RESOURCES IN LEARNING ENGLISH HISTORICAL TEXTS READING." Vestnik of Astrakhan State Technical University 2021, no. 1 (May 31, 2021): 56–60. http://dx.doi.org/10.24143/1812-9498-2021-1-56-60.

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The article considers reading as one of the most important types of communicative and cognitive activity. Reading is a means of information and educational activities, as well as a means of studying the national language and languages and cultures of other peoples. With the development of the Internet, the opportunities for learning foreign languages, including English, have increased significantly. As English is spoken internationally, the Internet space is filled with information in English, which also fulfills the teaching role at the educational institutions. There have been considered the online resources that can help in teaching and learning a foreign language

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McCrudden, Matthew T., Linh Huynh, Bailing Lyu, and Jonna M. Kulikowich. "Bridging Inferences and Learning from Multiple Complementary Texts." Discourse Processes 58, no. 5-6 (May 25, 2021): 529–48. http://dx.doi.org/10.1080/0163853x.2021.1924586.

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Zhao, Di, Jian Wang, Hongfei Lin, Yonghe Chu, Yan Wang, Yijia Zhang, and Zhihao Yang. "Sentence representation with manifold learning for biomedical texts." Knowledge-Based Systems 218 (April 2021): 106869. http://dx.doi.org/10.1016/j.knosys.2021.106869.

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Abdivalieva, G. A. "THE ROLE OF AUDIO TEXTS IN ONLINE LEARNING." Современные проблемы науки и образования (Modern Problems of Science and Education), no. 4 2021 (2021): 6. http://dx.doi.org/10.17513/spno.30966.

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Alkaoud, Mohamed, and Mairaj Syed. "Learning to Identify Narrators in Classical Arabic Texts." Procedia Computer Science 189 (2021): 335–42. http://dx.doi.org/10.1016/j.procs.2021.05.109.

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Ariyana, Ariyana, Enawar Enawar, Intan Sari Ramdhani, and Agus Sulaeman. "The Application of Discovery Learning Models in Learning to Write Descriptive Texts." Journal of English Education and Teaching 4, no. 3 (September 2, 2020): 401–12. http://dx.doi.org/10.33369/jeet.4.3.401-412.

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The purpose of this study was to describe the application of discovery learning models in learning to write descriptive texts. The research method uses descriptive methods. Data collection techniques using observation techniques, test writing descriptive text, and documentation. Data analysis techniques by reducing data, data presentation, data interpretation, conclusions. The results showed that the steps of the discovery learning model in learning to write descriptive texts are to describe topics, ask questions, formulate hypotheses, carry out investigations, conduct observations, organize or analyze, and present the results of investigations. Then, the students' ability to write descriptive text obtained an average value of 76.8 with good criteria after applying the discovery learning model. The learning to write descriptive text is carried out to instill the value of responsibility to students and improve the ability to write text descriptive students based on text, images, and places, so students are not passive.

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Miššíková, Gabriela. "Learning Culture through Text Analysis: Semantic and pragmatic foundations for working with texts." Journal of Linguistic and Intercultural Education 2, no. 1 (June 15, 2009): 63–76. http://dx.doi.org/10.29302/jolie.2009.2.1.5.

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Dutke, Stephan, Anna Christina Grefe, and Claudia Leopold. "Learning from scientific texts: personalizing the text increases transfer performance and task involvement." European Journal of Psychology of Education 31, no. 4 (December 30, 2015): 499–513. http://dx.doi.org/10.1007/s10212-015-0281-6.

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Journal articles on the topic 'Learning texts'

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