810,989 research outputs found
Factors Affecting Teacher Readiness for Online Learning (TROL) in Early Childhood Education: TISE and TPACK
This study aims to find empirical information about the effect of Technological Pedagogical Content Knowledge (TPACK), and Technology Integration Self Efficacy (TISE) on Teacher Readiness for Online Learning (TROL). This study uses a quantitative survey method with path analysis techniques. This study measures the readiness of kindergarten teachers in distance learning in Tanah Datar Regency, West Sumatra Province, Indonesia with a sampling technique using simple random sampling involving 105 teachers. Empirical findings reveal that; 1) there is a direct positive effect of Technology Integration Self Efficacy on Teacher Readiness for Online Learning; 2) there is a direct positive effect of PACK on Teacher Readiness for Online Learning; 3) there is a direct positive effect of Technology Integration Self Efficacy on TPACK. If want to improve teacher readiness for online learning, Technological Pedagogical Content Knowledge (TPACK) must be improved by paying attention to Technology Integration Self Efficacy (TISE).
Keywords: TROL, TPACK, TISE, Early Childhood Education
References:
Abbitt, J. T. (2011). An Investigation of the Relationship between Self-Efficacy Beliefs about Technology Integration and Technological Pedagogical Content Knowledge (TPACK) among Preservice Teachers. Journal of Digital Learning in Teacher Education, 27(4), 134–143.
Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1813180
Adnan, M. (2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://doi.org/10.33902/JPSP.2020261309
Alqurashi, E. (2016). Self-Efficacy in Online Learning Environments: A Literature Review. Contemporary Issues in Education Research (CIER), 9(1), 45–52. https://doi.org/10.19030/cier.v9i1.9549
Amir, H. (2016). Korelasi Pengaruh Faktor Efikasi Diri Dan Manajemen Diri Terhadap Motivasi Berprestasi Pada Mahasiswa Pendidikan Kimia Unversitas Bengkulu. Manajer Pendidikan, 10(4).
Anderson, T. (2008). The theory and practice of online learning. Athabasca University Press.
Anggraeni, N., Ridlo, S., & Setiati, N. (2018). The Relationship Between TISE and TPACK among Prospective Biology Teachers of UNNES. Journal of Biology Education, 7(3), 305–311. https://doi.org/10.15294/jbe.v7i3.26021
Ariani, D. N. (2015). Hubungan antara Technological Pedagogical Content Knowledge dengan Technology Integration Self Efficacy Guru Matematika di Sekolah Dasar. Muallimuna: Jurnal Madrasah Ibtidaiyah, 1(1), 79–91.
Birisci, S., & Kul, E. (2019). Predictors of Technology Integration Self-Efficacy Beliefs of Preservice Teachers. Contemporary Educational Technology, 10(1). https://doi.org/10.30935/cet.512537
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S. R., Al-freih, M., Pete, J., Olcott, D., Rodes, V., Aranciaga, I., Bali, M., Alvarez, A. V, Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., Coëtlogon, P. De, … Paskevicius, M. (2020). UVicSPACE: Research & Learning Repository Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126.
Brinkley-Etzkorn, K. E. (2018). Learning to teach online: Measuring the influence of faculty development training on teaching effectiveness through a TPACK lens. The Internet and Higher Education, 38, 28–35. https://doi.org/10.1016/j.iheduc.2018.04.004
Butnaru, G. I., Niță, V., Anichiti, A., & Brînză, G. (2021). The effectiveness of online education during covid 19 pandemic—A comparative analysis between the perceptions of academic students and high school students from romania. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095311
Carliner, S. (2003). Modeling information for three-dimensional space: Lessons learned from museum exhibit design. Technical Communication, 50(4), 554–570.
Cengiz, C. (2015). The development of TPACK, Technology Integrated Self-Efficacy and Instructional Technology Outcome Expectations of pre-service physical education teachers. Asia-Pacific Journal of Teacher Education, 43(5), 411–422. https://doi.org/10.1080/1359866X.2014.932332
Chou, P., & Ph, D. (2012). Effect of Students ’ Self -Directed Learning Abilities on Online Learning Outcomes: Two Exploratory Experiments in Electronic Engineering Department of Education. 2(6), 172–179.
Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Burton, R., Glowatz, M., Magni, P. A., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1). https://doi.org/10.37074/jalt.2020.3.1.7
Dolighan, T., & Owen, M. (2021). Teacher efficacy for online teaching during the COVID-19 pandemic. Brock Education Journal, 30(1), 95. https://doi.org/10.26522/brocked.v30i1.851
Dong, Y., Chai, C. S., Sang, G.-Y., Koh, J. H. L., & Tsai, C.-C. (2015). Exploring the Profiles and Interplays of Pre-service and In-service Teachers’ Technological Pedagogical Content Knowledge (TPACK) in China. International Forum of Educational Technology & Society, 18(1), 158–169.
Donitsa-Schmidt, S., & Ramot, R. (2020). Opportunities and challenges: Teacher education in Israel in the Covid-19 pandemic. Journal of Education for Teaching, 46(4), 586–595. https://doi.org/10.1080/02607476.2020.1799708
Elas, N. I. B., Majid, F. B. A., & Narasuman, S. A. (2019). Development of Technological Pedagogical Content Knowledge (TPACK) For English Teachers: The Validity and Reliability. International Journal of Emerging Technologies in Learning (IJET), 14(20), 18. https://doi.org/10.3991/ijet.v14i20.11456
Ghozali, I. (2011). Aplikasi multivariate dengan program IBM SPSS 19. Badan Penerbit Universitas Diponegoro.
Giles, R. M., & Kent, A. M. (2016). An Investigation of Preservice Teachers ’ Self-Efficacy for Teaching with Technology. 1(1), 32–40. https://doi.org/10.20849/aes.v1i1.19
Gil-flores, J., & Rodríguez-santero, J. (2017). Computers in Human Behavior Factors that explain the use of ICT in secondary-education classrooms: The role of teacher characteristics and school infrastructure. Computers in Human Behavior, 68, 441–449. https://doi.org/10.1016/j.chb.2016.11.057
Habibi, A., Yusop, F. D., & Razak, R. A. (2019). The role of TPACK in affecting pre-service language teachers’ ICT integration during teaching practices: Indonesian context. Education and Information Technologies. https://doi.org/10.1007/s10639-019-10040-2
Harris, J. B., & Hofer, M. J. (2011). Technological Pedagogical Content Knowledge (TPACK) in Action. Journal of Research on Technology in Education, 43(3), 211–229. https://doi.org/10.1080/15391523.2011.10782570
Hatlevik, I. K. R., & Hatlevik, O. E. (2018). Examining the relationship between teachers’ ICT self-efficacy for educational purposes, collegial collaboration, lack of facilitation and the use of ICT in teaching practice. Frontiers in Psychology, 9(JUN), 1–8. https://doi.org/10.3389/fpsyg.2018.00935
Hung, M. L. (2016). Teacher readiness for online learning: Scale development and teacher perceptions. Computers and Education, 94, 120–133. https://doi.org/10.1016/j.compedu.2015.11.012
Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers and Education, 55(3), 1080–1090. https://doi.org/10.1016/j.compedu.2010.05.004
Juanda, A., Shidiq, A. S., & Nasrudin, D. (2021). Teacher Learning Management: Investigating Biology Teachers’ TPACK to Conduct Learning During the Covid-19 Outbreak. Jurnal Pendidikan IPA Indonesia, 10(1), 48–59. https://doi.org/10.15294/jpii.v10i1.26499
Karatas, M. A.-K. (2020). COVID - 19 Pandemisinin Toplum Psikolojisine Etkileri ve Eğitime Yansımaları. Journal of Turkish Studies, Volume 15(Volume 15 Issue 4), 1–13. https://doi.org/10.7827/TurkishStudies.44336
Kaymak, Z. D., & Horzum, M. B. (2013). Relationship between online learning readiness and structure and interaction of online learning students. Kuram ve Uygulamada Egitim Bilimleri, 13(3), 1792–1797. https://doi.org/10.12738/estp.2013.3.1580
Keser, H., Karaoğlan Yılmaz, F. G., & Yılmaz, R. (2015). TPACK Competencies and Technology Integration Self-Efficacy Perceptions of Pre-Service Teachers. Elementary Education Online, 14(4), 1193–1207. https://doi.org/10.17051/io.2015.65067
Kim, J. (2020). Learning and Teaching Online During Covid-19: Experiences of Student Teachers in an Early Childhood Education Practicum. International Journal of Early Childhood, 52(2), 145–158. https://doi.org/10.1007/s13158-020-00272-6
Koehler, M. J., Mishra, P., & Cain, W. (2013). What is Technological Pedagogical Content Knowledge (TPACK)? Journal of Education, 193(3), 13–19. https://doi.org/10.1177/002205741319300303
Lee, Y., & Lee, J. (2014). Enhancing pre-service teachers’ self-efficacy beliefs for technology integration through lesson planning practice. Computers and Education, 73, 121–128. https://doi.org/10.1016/j.compedu.2014.01.001
Mallillin, L. L. D., Mendoza, L. C., Mallillin, J. B., Felix, R. C., & Lipayon, I. C. (2020). Implementation and Readiness of Online Learning Pedagogy: A Transition To Covid 19 Pandemic. European Journal of Open Education and E-Learning Studies, 5(2), 71–90. https://doi.org/10.46827/ejoe.v5i2.3321
Mishra, P. (2019). Considering Contextual Knowledge: The TPACK Diagram Gets an Upgrade. Journal of Digital Learning in Teacher Education, 35(2), 76–78. https://doi.org/10.1080/21532974.2019.1588611
Moorhouse, B. L. (2020). Adaptations to a face-to-face initial teacher education course ‘forced’ online due to the COVID-19 pandemic. Journal of Education for Teaching, 46(4), 609–611. https://doi.org/10.1080/02607476.2020.1755205
Mulyadi, D., Wijayatingsih, T. D., Budiastuti, R. E., Ifadah, M., & Aimah, S. (2020). Technological Pedagogical and Content Knowledge of ESP Teachers in Blended Learning Format. International Journal of Emerging Technologies in Learning (IJET), 15(06), 124. https://doi.org/10.3991/ijet.v15i06.11490
Murtaza, G., Mahmood, K., & Fatima, N. (2021). Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students The Journal of Academic Librarianship Readiness for Online Learning during COVID-19 pandemic: A survey of Pakistani LIS students. The Journal of Academic Librarianship, 47(3), 102346. https://doi.org/10.1016/j.acalib.2021.102346
Mustika, M., & Sapriya. (2019). Kesiapan Guru IPS dalam E-learning Berdasarkan: Survei melalui Pendekatan TPACK. 32–35. https://doi.org/10.1145/3306500.3306566
Niess, M. L. (2011). Investigating TPACK: Knowledge Growth in Teaching with Technology. Journal of Educational Computing Research, 44(3), 299–317. https://doi.org/10.2190/EC.44.3.c
Oketch, & Otchieng, H. (2013). University of Nairobi, H. A. (2013). E-Learning Readiness Assessment Model in Kenyas’ Higher Education Institutions: A Case Study of University of Nairobi by: Oketch, Hada Achieng a Research Project Submitted in Partial Fulfillment of the Requirement of M. October.
Pamuk, S., Ergun, M., Cakir, R., Yilmaz, H. B., & Ayas, C. (2015). Exploring relationships among TPACK components and development of the TPACK instrument. Education and Information Technologies, 20(2), 241–263. https://doi.org/10.1007/s10639-013-9278-4
Paraskeva, F., Bouta, H., & Papagianni, A. (2008). Individual characteristics and computer self-efficacy in secondary education teachers to integrate technology in educational practice. Computers and Education, 50(3), 1084–1091. https://doi.org/10.1016/j.compedu.2006.10.006
Putro, S. T., Widyastuti, M., & Hastuti, H. (2020). Problematika Pembelajaran di Era Pandemi COVID-19 Stud Kasus: Indonesia, Filipina, Nigeria, Ethiopia, Finlandia, dan Jerman. Geomedia Majalah Ilmiah Dan Informasi Kegeografian, 18(2), 50–64.
Qudsiya, R., Widiyaningrum, P., & Setiati, N. (2018). The Relationship Between TISE and TPACK among Prospective Biology Teachers of UNNES. Journal of Biology Education, 7(3), 305–311. https://doi.org/10.15294/jbe.v7i3.26021
Reflianto, & Syamsuar. (2018). Pendidikan dan Tantangan Pembelajaran Berbasis Teknologi Informasi di Era Revolusi Industri 4.0. Jurnal Ilmiah Teknologi Pendidikan, 6(2), 1–13.
Reski, A., & Sari, K. (2020). Analisis Kemampuan TPACK Guru Fisika Se-Distrik Merauke. Jurnla Kreatif Online, 8(1), 1–8.
Ruggiero, D., & Mong, C. J. (2015). The teacher technology integration experience: Practice and reflection in the classroom. Journal of Information Technology Education, 14.
Santika, V., Indriayu, M., & Sangka, K. B. (2021). Profil TPACK Guru Ekonomi di Indonesia sebagai Pendekatan Integrasi TIK selama Pembelajaran Jarak Jauh pada Masa Pandemi Covid-19. Duconomics Sci-Meet (Education & Economics Science Meet), 1, 356–369. https://doi.org/10.37010/duconomics.v1.5470
Semiz, K., & Ince, M. L. (2012). Pre-service physical education teachers’ technological pedagogical content knowledge, technology integration self-efficacy and instructional technology outcome expectations. Australasian Journal of Educational Technology, 28(7). https://doi.org/10.14742/ajet.800
Senthilkumar, Sivapragasam, & Senthamaraikannan. (2014). Role of ICT in Teaching Biology. International Journal of Research, 1(9), 780–788.
Setiaji, B., & Dinata, P. A. C. (2020). Analisis kesiapan mahasiswa jurusan pendidikan fisika menggunakan e-learning dalam situasi pandemi Covid-19 Analysis of e-learning readiness on physics education students during Covid-19 pandemic. 6(1), 59–70.
Siagian, H. S., Ritonga, T., & Lubis, R. (2021). Analisis Kesiapan Belajar Daring Siswa Kelas Vii Pada Masa Pandemi Covid-19 Di Desa Simpang. JURNAL MathEdu (Mathematic Education Journal), 4(2), 194–201.
Sintawati, M., & Indriani, F. (2019). Pentingnya Technological Pedagogical Content Knowledge (TPACK) Guru di Era Revolusi Industri 4.0. Seminar Nasional Pagelaran Pendidikan Dasar Nasional (PPDN), 1(1), 417–422.
Sojanah, J., Suwatno, Kodri, & Machmud, A. (2021). Factors affecting teachers’ technological pedagogical and content knowledge (A survey on economics teacher knowledge). Cakrawala Pendidikan, 40(1), 1–16. https://doi.org/10.21831/cp.v40i1.31035
Subhan, M. (2020). Analisis Penerapan Technological Pedagogical Content Knowledge Pada Proses Pembelajaran Kurikulum 2013 di Kelas V. International Journal of Technology Vocational Education and Training, 1(2), 174–179.
Sum, T. A., & Taran, E. G. M. (2020). Kompetensi Pedagogik Guru PAUD dalam Perencanaan dan Pelaksanaan Pembelajaran. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 4(2), 543. https://doi.org/10.31004/obsesi.v4i2.287
Suryawati, E., Firdaus, L. N., & Yosua, H. (2014). Analisis keterampilan technological pedagogical content knowledge (TPCK) guru biologi SMA negeri kota Pekanbaru. Jurnal Biogenesis, 11(1), 67-72.
Suyamto, J., Masykuri, M., & Sarwanto, S. (2020). Analisis Kemampuan Tpack (Technolgical, Pedagogical, and Content, Knowledge) Guru Biologi Sma Dalam Menyusun Perangkat Pembelajaran Materi Sistem Peredaran Darah. INKUIRI: Jurnal Pendidikan IPA, 9(1), 46. https://doi.org/10.20961/inkuiri.v9i1.41381
Tiara, D. R., & Pratiwi, E. (2020). Pentingnya Mengukur Kesiapan Guru Sebagai Dasar Pembelajaran Daring. Jurnal Golden Age, 04(2), 362–368.
Trionanda, S. (2021). Analisis kesiapan dan pelaksanaan pembelajaran matematika jarak jauh berdasarkan profil TPACK di SD Katolik Tanjungpinang tahun ajaran 2020 / 2021. In Prosiding Seminar Nasional Matematika Dan Pendidikan Matematika, 6, 69–76.
Tsai, C.-C., & Chai, C. S. (2012). The ‘third’-order barrier for technology-integration instruction: Implications for teacher education. Australasian Journal of Educational Technology, 28(6). https://doi.org/10.14742/ajet.810
Wahyuni, F. T. (2019). Hubungan Antara Technological Pedagogical Content Knowledge (Tpack) Dengan Technology Integration Self Efficacy (Tise) Guru Matematika Di Madrasah Ibtidaiyah. Jurnal Pendidikan Matematika (Kudus), 2(2), 109–122. https://doi.org/10.21043/jpm.v2i2.6358
Wang, L., Ertmer, P. A., & Newby, T. J. (2014). Journal of Research on Technology in Education Increasing Preservice Teachers’ Self-Efficacy Beliefs for Technology Integration. Journal of Research on Technology in Education, 36(3), 37–41. https://doi.org/10.1080/15391523.2004.10782414
Warden, C. A., Yi-Shun, W., Stanworth, J. O., & Chen, J. F. (2020). Millennials’ technology readiness and self-efficacy in online classes. Innovations in Education and Teaching International, 00(00), 1–11. https://doi.org/10.1080/14703297.2020.1798269
Widarjono, A. (2015). Analisis Multivariat Terapan edisi kedua. UPP STIM YKPN.
Wiresti, R. D. (2021). Analisis Dampak Work from Home pada Anak Usia Dini di Masa Pandemi Covid-19. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 5(1), 641653. https://doi.org/10.31004/obsesi.v5i1.563
Yildiz Durak, H. (2019). Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interactive Learning Environments, 1–27. https://doi.org/10.1080/10494820.2019.1619591
Yudha, F., Aziz, A., & Tohir, M. (2021). Pendampingan Siswa Terdampak Covid-19 Melalui Media Animasi Sebagai Inovasi Pembelajaran Online. JMM (Jurnal Masyarakat Mandiri), 5(3), 964–978.
Yurdugül, H., & Demir, Ö. (2017). An investigation of Pre-service Teachers’ Readiness for E-learning at Undergraduate Level Teacher Training Programs: The Case of Hacettepe University. The Case of Hacettepe University.
 
KM Maturity Factors Affecting High Performance in Universities
This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge
management on high performance. This study was applied on Al-Quds Open University in Gaza strip, Palestine. Asian
productivity organization model was applied to measure KMM. Second dimension which assess high performance was
developed by the authors. The controlled sample was (306). Several statistical tools were used for data analysis and hypotheses
testing, including reliability Correlation using Cronbach’s alpha, “ANOVA”, Simple Linear Regression and Step Wise
Regression.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM
assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The
most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and
Innovation. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is
the first comparative study in the universities of Palestine explores the status of KMM using the Asian productivity Model
Cross-Language Plagiarism Detection
Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.This work was partially supported by the TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 project and the CONACyT-Mexico 192021 grant.Potthast, M.; Barrón Cedeño, LA.; Stein, B.; Rosso, P. (2011). Cross-Language Plagiarism Detection. Language Resources and Evaluation. 45(1):45-62. https://doi.org/10.1007/s10579-009-9114-zS4562451Ballesteros, L. A. (2001). Resolving ambiguity for cross-language information retrieval: A dictionary approach. PhD thesis, University of Massachusetts Amherst, USA, Bruce Croft.Barrón-Cedeño, A., Rosso, P., Pinto, D., & Juan A. (2008). On cross-lingual plagiarism analysis using a statistical model. In S. Benno, S. Efstathios, & K. Moshe (Eds.), ECAI 2008 workshop on uncovering plagiarism, authorship, and social software misuse (PAN 08) (pp. 9–13). Patras, Greece.Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities, 3, 1–8.Berger, A., & Lafferty, J. (1999). Information retrieval as statistical translation. In SIGIR’99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (vol. 4629, pp. 222–229). Berkeley, California, United States: ACM.Brin, S., Davis, J., & Garcia-Molina, H. (1995). Copy detection mechanisms for digital documents. In SIGMOD ’95 (pp. 398–409). New York, NY, USA: ACM Press.Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.Ceska, Z., Toman, M., & Jezek, K. (2008). Multilingual plagiarism detection. In AIMSA’08: Proceedings of the 13th international conference on artificial intelligence (pp. 83–92). Berlin, Heidelberg: Springer.Clough, P. (2003). Old and new challenges in automatic plagiarism detection. National UK Plagiarism Advisory Service, http://www.ir.shef.ac.uk/cloughie/papers/pas_plagiarism.pdf .Dempster A. P., Laird N. M., Rubin D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Dumais, S. T., Letsche, T. A., Littman, M. L., & Landauer, T. K. (1997). Automatic cross-language retrieval using latent semantic indexing. In D. Hull & D. Oard (Eds.), AAAI-97 spring symposium series: Cross-language text and speech retrieval (pp. 18–24). Stanford University, American Association for Artificial Intelligence.Gabrilovich, E., & Markovitch, S. (2007). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th international joint conference for artificial intelligence, Hyderabad, India.Hoad T. C., & Zobel, J. (2003). Methods for identifying versioned and plagiarised documents. American Society for Information Science and Technology, 54(3), 203–215.Levow, G.-A., Oard, D. W., & Resnik, P. (2005). Dictionary-based techniques for cross-language information retrieval. Information Processing & Management, 41(3), 523–547.Littman, M., Dumais, S. T., & Landauer, T. K. (1998). Automatic cross-language information retrieval using latent semantic indexing. In Cross-language information retrieval, chap. 5 (pp. 51–62). Kluwer.Maurer, H., Kappe, F., & Zaka, B. (2006). Plagiarism—a survey. Journal of Universal Computer Science, 12(8), 1050–1084.McCabe, D. (2005). Research report of the Center for Academic Integrity. http://www.academicintegrity.org .Mcnamee, P., & Mayfield, J. (2004). Character N-gram tokenization for European language text retrieval. Information Retrieval, 7(1–2), 73–97.Meyer zu Eissen, S., & Stein, B. (2006). Intrinsic plagiarism detection. In M. Lalmas, A. MacFarlane, S. M. Rüger, A. Tombros, T. Tsikrika, & A. Yavlinsky (Eds.), Proceedings of the European conference on information retrieval (ECIR 2006), volume 3936 of Lecture Notes in Computer Science (pp. 565–569). Springer.Meyer zu Eissen, S., Stein, B., & Kulig, M. (2007). Plagiarism detection without reference collections. In R. Decker & H. J. Lenz (Eds.), Advances in data analysis (pp. 359–366), Springer.Och, F. J., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.Pinto, D., Juan, A., & Rosso, P. (2007). Using query-relevant documents pairs for cross-lingual information retrieval. In V. Matousek & P. Mautner (Eds.), Lecture Notes in Artificial Intelligence (pp. 630–637). Pilsen, Czech Republic.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to cross-lingual natural language tasks. Journal of Algorithms, 64(1), 51–60.Potthast, M. (2007). Wikipedia in the pocket-indexing technology for near-duplicate detection and high similarity search. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 909–909). ACM.Potthast, M., Stein, B., & Anderka, M. (2008). A Wikipedia-based multilingual retrieval model. In C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, & R. W. White (Eds.), 30th European conference on IR research, ECIR 2008, Glasgow , volume 4956 LNCS of Lecture Notes in Computer Science (pp. 522–530). Berlin: Springer.Pouliquen, B., Steinberger, R., & Ignat, C. (2003a). Automatic annotation of multilingual text collections with a conceptual thesaurus. In Proceedings of the workshop ’ontologies and information extraction’ at the Summer School ’The Semantic Web and Language Technology—its potential and practicalities’ (EUROLAN’2003) (pp. 9–28), Bucharest, Romania.Pouliquen, B., Steinberger, R., & Ignat, C. (2003b). Automatic identification of document translations in large multilingual document collections. In Proceedings of the international conference recent advances in natural language processing (RANLP’2003) (pp. 401–408). Borovets, Bulgaria.Stein, B. (2007). Principles of hash-based text retrieval. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 527–534). ACM.Stein, B. (2005). Fuzzy-fingerprints for text-based information retrieval. In K. Tochtermann & H. Maurer (Eds.), Proceedings of the 5th international conference on knowledge management (I-KNOW 05), Graz, Journal of Universal Computer Science. (pp. 572–579). Know-Center.Stein, B., & Anderka, M. (2009). Collection-relative representations: A unifying view to retrieval models. In A. M. Tjoa & R. R. Wagner (Eds.), 20th International conference on database and expert systems applications (DEXA 09) (pp. 383–387). IEEE.Stein, B., & Meyer zu Eissen, S. (2007). Intrinsic plagiarism analysis with meta learning. In B. Stein, M. Koppel, & E. Stamatatos (Eds.), SIGIR workshop on plagiarism analysis, authorship identification, and near-duplicate detection (PAN 07) (pp. 45–50). CEUR-WS.org.Stein, B., & Potthast, M. (2007). Construction of compact retrieval models. In S. Dominich & F. Kiss (Eds.), Studies in theory of information retrieval (pp. 85–93). Foundation for Information Society.Stein, B., Meyer zu Eissen, S., & Potthast, M. (2007). Strategies for retrieving plagiarized documents. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 825–826). ACM.Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., & Varga, D. (2006). The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. In Proceedings of the 5th international conference on language resources and evaluation (LREC’2006).Steinberger, R., Pouliquen, B., & Ignat, C. (2004). Exploiting multilingual nomenclatures and language-independent text features as an interlingua for cross-lingual text analysis applications. In Proceedings of the 4th Slovenian language technology conference. Information Society 2004 (IS’2004).Vinokourov, A., Shawe-Taylor, J., & Cristianini, N. (2003). Inferring a semantic representation of text via cross-language correlation analysis. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS-02: Advances in neural information processing systems (pp. 1473–1480). MIT Press.Yang, Y., Carbonell, J. G., Brown, R. D., & Frederking, R. E. (1998). Translingual information retrieval: Learning from bilingual corpora. Artificial Intelligence, 103(1–2), 323–345
Interoperability, Trust Based Information Sharing Protocol and Security: Digital Government Key Issues
Improved interoperability between public and private organizations is of key
significance to make digital government newest triumphant. Digital Government
interoperability, information sharing protocol and security are measured the
key issue for achieving a refined stage of digital government. Flawless
interoperability is essential to share the information between diverse and
merely dispersed organisations in several network environments by using
computer based tools. Digital government must ensure security for its
information systems, including computers and networks for providing better
service to the citizens. Governments around the world are increasingly
revolving to information sharing and integration for solving problems in
programs and policy areas. Evils of global worry such as syndrome discovery and
manage, terror campaign, immigration and border control, prohibited drug
trafficking, and more demand information sharing, harmonization and cooperation
amid government agencies within a country and across national borders. A number
of daunting challenges survive to the progress of an efficient information
sharing protocol. A secure and trusted information-sharing protocol is required
to enable users to interact and share information easily and perfectly across
many diverse networks and databases globally.Comment: 20 page
Bibliometric studies on single journals: a review
This paper covers a total of 82 bibliometric studies on single journals (62 studies cover unique titles) published between 1998 and 2008 grouped into the following fields; Arts, Humanities and Social Sciences (12 items); Medical and Health Sciences (19 items); Sciences and Technology (30 items) and Library and Information Sciences (21 items). Under each field the studies are described in accordance to their geographical location in the following order, United Kingdom, United States and Americana, Europe, Asia (India, Africa and Malaysia). For each study, elements described are (a) the journal’s publication characteristics and indexation information; (b) the objectives; (c) the sampling and bibliometric measures used; and (d) the results observed. A list of journal titles studied is appended. The results show that (a)bibliometric studies cover journals in various fields; (b) there are several revisits of some journals which are considered important; (c) Asian and African contributions is high (41.4 of total studies; 43.5 covering unique titles), United States (30.4 of total; 31.0 on unique titles), Europe (18.2 of total and 14.5 on unique titles) and the United Kingdom (10 of total and 11 on unique titles); (d) a high number of bibliometrists are Indians and as such coverage of Indian journals is high (28 of total studies; 30.6 of unique titles); and (e) the quality of the journals and their importance either nationally or internationally are inferred from their indexation status
User involvement in healthcare technology development and assessment: Structured literature review
Purpose – Medical device users are one of the principal stakeholders of medical device technologies. User involvement in medical device technology development and assessment is central to meet their needs.
Design/methodology/approach – A structured review of literature, published from 1980 to 2005 in peer-reviewed journals, was carried out from social science perspective to investigate the practice of user involvement in the development and assessment of medical device technologies. This was followed by qualitative thematic analysis.
Findings – It is found that users of medical devices include clinicians, patients, carers and others. Different kinds of medical devices are developed and assessed by user involvement. The user involvement occurs at different stages of the medical device technology lifecycle and the degree of user involvement is in the order of design stage > testing and trials stage > deployment stage > concept stage. Methods most commonly used for capturing users’ perspectives are usability tests, interviews and questionnaire surveys.
Research limitations/implications – We did not review the relevant literature published in engineering, medical and nursing fields, which might have been useful.
Practical implications – Consideration of the users’ characteristics and the context of medical device use is critical for developing and assessing medical device technologies from users’ perspectives.
Originality/value – This study shows that users of medical device technologies are not homogeneous but heterogeneous, in several aspects, and their needs, skills and working environments vary. This is important consideration for incorporating users’ perspectives in medical device technologies.
Paper type: Literature review
Practices for strategic capacity management in Malaysian manufacturing firms
While the notion of manufacturing capabilities is a long-standing notion in research on operations management, its actual implementation and management has been hardly researched. Five case studies in Malaysia offered the opportunity to examine the practice of manufacturing managers with regard to strategic capability management. The data collection and analysis was structured by using the notion of Strategic Capacity Management. Whereas traditionally literature has demonstrated the beneficial impact of an appropriate manufacturing strategy on the business strategy and performance, the study highlights the difficulty of managers to set the strategy, let alone implementing it. This is partly caused by the immense pressure of customers in these dominantly Make-To-Order environments for SMEs. Current concepts for manufacturing capabilities have insufficiently accounted this phenomenon and an outline of a research agenda is presented
New product development in an emerging economy: analysing the role of supplier involvement practices by using Bayesian Markov chain Monte Carlo technique
The research question is whether the positive relationship found between supplier involvement practices and new product development performances in developed economies also holds in emerging economies. The role of supplier involvement practices in new product development performance is yet to be substantially investigated in the emerging economies (other than China). This premise was examined by distributing a survey instrument (Jayaram’s (2008) published survey instrument that has been utilised in developed economies) to Malaysian manufacturing companies. To gauge the relationship between the supplier involvement practices and new product development (NPD) project performance of 146 companies, structural equation modelling was adopted. Our findings prove that supplier involvement practices have a significant positive impact on NPD project performance in an emerging economy with respect to quality objectives, design objectives, cost objectives, and “time-to-market” objectives. Further analysis using the Bayesian Markov Chain Monte Carlo algorithm, yielding a more credible and feasible differentiation, confirmed these results (even in the case of an emerging economy) and indicated that these practices have a 28% impact on variance of NPD project performance. This considerable effect implies that supplier involvement is a must have, although further research is needed to identify the contingencies for its practices
- …