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Relevant bibliographies by topics / Knowledge differences

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Academic literature on the topic 'Knowledge differences'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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Journal articles on the topic "Knowledge differences"

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Lynn, Richard, Paul Irwing, and Thomas Cammock. "Sex differences in general knowledge." Intelligence 30, no. 1 (January 2001): 27–39. http://dx.doi.org/10.1016/s0160-2896(01)00064-2.

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Donald, Janet G. "Disciplinary differences in knowledge validation." New Directions for Teaching and Learning 1995, no. 64 (1995): 6–17. http://dx.doi.org/10.1002/tl.37219956404.

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Rambla, Xavier. "Domestic Knowledge, Inequalities and Differences." European Journal of Women's Studies 7, no. 2 (May 2000): 189–207. http://dx.doi.org/10.1177/135050680000700201.

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Beatty, William W., and Alexander I. Tr�ster. "Gender differences in geographical knowledge." Sex Roles 16, no. 11-12 (June 1987): 565–90. http://dx.doi.org/10.1007/bf00300374.

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Ackerman, Phillip L., Kristy R. Bowen, Margaret E. Beier, and Ruth Kanfer. "Determinants of individual differences and gender differences in knowledge." Journal of Educational Psychology 93, no. 4 (December 2001): 797–825. http://dx.doi.org/10.1037/0022-0663.93.4.797.

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Hallin, Carina Antonia, Torvald Øgaard, and Einar Marnburg. "Exploring Qualitative Differences in Knowledge Sources." International Journal of Knowledge Management 5, no. 4 (October 2009): 1–25. http://dx.doi.org/10.4018/jkm.2009062901.

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Focusing on knowledge management (KM) and strategic decision making in service businesses through the constructs of strategic capital and knowledge sharing, the study investigates qualitative differences in domain-specific knowledge of frontline employees and executives. The study draws on cognitive theory and investigates the extent to which the knowledge of these subject groups is correct with respect to incorporating intuitive judgments by various employee groups into forecasting and following strategic decision making. The authors carried out this investigation through an exploratory study of the subject groups’ confidence and accuracy (CA) performance in a constructed knowledge-based forecasting setting. The groups’ intuitive judgmental performances were examined when predicting uncertain business and industry-related outcomes. The authors surveyed 39 executives and 38 frontline employees in 12 hotels. The analysis is based on a between-participants design. The results from this setting do not fully confirm findings in earlier CA studies. Their results indicate that there are no significant differences in the accuracy of executives (as experts) and frontline employees (as novices). Although executives demonstrate overconfidence in their judgments and frontline employees demonstrate under confidence, in line with earlier CA theory of experts and novices, the differences we find are not significant. Similarly, the CA calibration performance difference between the two groups is not significant. They suggest, among other reasons, that our findings differ from earlier CA studies because of organizational politics and culture by power distance, social capital, misuse of knowledge and the size of the business.

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GAMBARDELLA, ALFONSO, and MARCO S. GIARRATANA. "LOCALIZED KNOWLEDGE SPILLOVERS AND WAGE DIFFERENCES." Academy of Management Proceedings 2010, no. 1 (August 2010): 1–6. http://dx.doi.org/10.5465/ambpp.2010.54493453.

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Kiyokawa, Sachiko, Zoltán Dienes, Daisuke Tanaka, Ayumi Yamada, and Louise Crowe. "Cross cultural differences in unconscious knowledge." Cognition 124, no. 1 (July 2012): 16–24. http://dx.doi.org/10.1016/j.cognition.2012.03.009.

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Rolfhus, Eric L., and Phillip L. Ackerman. "Assessing individual differences in knowledge: Knowledge, intelligence, and related traits." Journal of Educational Psychology 91, no. 3 (1999): 511–26. http://dx.doi.org/10.1037/0022-0663.91.3.511.

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Majchrzak, Ann, Philip H. B. More, and Samer Faraj. "Transcending Knowledge Differences in Cross-Functional Teams." Organization Science 23, no. 4 (August 2012): 951–70. http://dx.doi.org/10.1287/orsc.1110.0677.

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Dissertations / Theses on the topic "Knowledge differences"

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Bowen, Kristy Rae. "Gender differences in knowledge." Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/28868.

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Lings, Pamela Margaret. "Subject differences in applying knowledge to learn." Thesis, University of Exeter, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361382.

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Persson, Paula. "Knowledge sharing while teleworking : How it occurs, differences and knowledge sharing barriers." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-80264.

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Knowledge is the resource that provides the best possibilities of being competitive since it increases while used and is difficult to copy. Knowledge sharing has been shown to increase organizational performance in both tangible and intangible measures. Therefore, it is important that employees of a research and development department in a large Swedish industry, that were recommended to telework in combination with short-time working due to covid-19, continue sharing knowledge. This study aimed to analyze how employees in a development department share knowledge while teleworking and how it differs from knowledge sharing in a traditional office. The phenomenon was analyzed in-depth, especially what prevents the knowledge from being shared and knowledge sharing barriers that exist during telework. The objective was to gain further knowledge of how departments can share knowledge efficiently while teleworking. A qualitative single case study was conducted and 8 semi-structured interviews were held with employees at the department. The findings suggest that knowledge sharing occurs with tools that are used in a traditional office and that is no difference in terms of whom employees share knowledge with, but the face-to-face meetings are missing. A further suggestion is that it occurs by less informal knowledge sharing and that the meetings tend to have a stricter agenda that is kept at a higher degree than when working in a traditional office. The knowledge sharing barriers identified were: challenging to express knowledge, time constraints, uncertainty which knowledge to share with whom, less informal places to share knowledge, lack of trust, differences in experience/knowledge/education, attitude, and culture & leadership. The results give companies in similar situations cognizance of how knowledge sharing occurs and differ when suddenly teleworking is required and knowledge sharing barriers that exist. These findings identify risks and difficulties of knowledge sharing when teleworking.

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Davenport, Brandy, Marc Fagelson, Marcy Kay Lau, and Saravanan Elangovan. "Public Knowledge and Opinions on Differences Between Hearing Professionals." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/63.

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Both audiologists and hearing instrument specialists (HIS) can assess hearing loss and fit hearing aids; consumers interested in purchasing hearing aids can choose the type of hearing professional they see. However, there are vast differences in required training and scope of practice for each professional and advanced test batteries, such as those associated with vestibular assessment and tinnitus management, may distinguish the two professional groups more thoroughly than standard hearing testing and hearing aid fitting. This study aimed to determine the knowledge and opinions the average individual possesses regarding differences between these two types of hearing professionals in order to identify areas in which audiologists can more effectively differentiate themselves from hearing instrument specialists. A one-time survey was administered at 3 locations to a total of 47 English-speaking participants over the age of 18. Locations included the 2018 Remote Area Medical event in Gray, TN; the Johnson City Senior Center, and the Bristol Public Library. The survey consisted of 8 demographic questions with 3 additional questions if the participant wore hearing aids, followed by 18 questions that covered “Diagnostics,” “Intervention,” and “Interpersonal Patient Interactions.” Responses were analyzed for trends in overall data as well as in sub-categories for gender, household income, education level, age, and hearing aid use. Overall, participants tended to believe audiologists were most likely to perform tasks in the “Diagnostic” category, while a majority of participants believed both professionals could perform tasks in the “Intervention” category. There was no clear trend within the “Interpersonal Patient Interactions” category. Most participants believed that either both professional groups, or only audiologists, could perform most tasks adequately, indicating subjects likely do understand differences between the two professionals. In analyzing results, two issues emerged. The first was that the majority of participants believed that they would not be likely to pay a reasonable price for hearing aids when visiting either professional. One reason may be that only a few hearing aid companies produce most of the world’s hearing aids, leaving little room for low-cost competition. The “bundled” method which most hearing healthcare providers use for selling hearing aids along with services might also exert an influence on the perception of hearing aid pricing and may not be clear to patients who try to understand the actual cost of devices and services. Another issue identified in this survey was access. A significant number of respondents believed it was difficult to make and attend appointments with either professional, signifying a possible deficit in this area in access to hearing healthcare. The barriers to access included income, availability of transportation, inconvenient provider hours, and lack of insurance coverage. The results suggested that audiologists and HISs face challenges when recommending personal hearing devices to support rehabilitative efforts. Additionally, the provision of services that utilize advanced diagnostic batteries, such as balance assessment and tinnitus services, may offer audiologists an opportunity to distinguish their practices from those managed by HISs.

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Luke, Anna K. "Examining differences in parent knowledge about pediatric food allergies." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1555324911098815.

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Laumann, Lisa L. "Adult age differences in vocabulary acquisition as a function of individual differences in working memory and prior knowledge." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=614.

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Thesis (Ph. D.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains vii, 78 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 34-38).

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Austin, Lydia B. (Lydia Bronwen). "Individual differences in knowledge representation and problem- solving performance in physics." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=41100.

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Concept mapping in college-level physics was investigated. The study was carried out in three parts. First, an attempt was made to validate concept mapping as a method of evaluating student learning at the junior college level (ages 16-21). Several measures were found to be sensitive to differences in students' achievement. Second, the effectiveness of concept mapping as an instructional strategy was investigated. It was found that the strategy led to improvement in multistep problem-solving performance but not in performance on single step problems. Third, the concept maps made by experts in the field were compared with the maps made by high achieving and average achieving students to see if this is yet another way in which high performance and expertise are related. It was found that the high achieving students made maps which more nearly resembled the maps made by experts than those made by average achieving students.

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Stafford, Judy Mary. "Expert-novice differences in resequencing as a function of knowledge base." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63888.

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Moreira, Rashidat Oladotun. "Age and Gender Differences in Attitudes and Knowledge about Alzheimer's Disease." Thesis, North Dakota State University, 2014. https://hdl.handle.net/10365/27382.

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The purpose of this study was to examine possible age and gender discrepancies in knowledge and attitudes towards individuals with Alzheimer's disease (AD). Data were taken from a Midwestern survey study of community-dwelling adults aged 18-88 (N=211). Participants were divided into two age groups: younger adults (ages 18-49), and older adults, encompassing the Baby Boom generation (ages 49+). The findings indicated that, relative to older adults, younger adults were: less likely to know someone with AD; less likely to make lifestyle changes to reduce their AD risk; and less factually knowledgeable about AD. However, younger adults reported more positive attitudes about AD. When demographic variables, knowing someone with AD, and knowledge of AD were examined simultaneously as predictors of attitudes, the following were significant: age, knowledge, and knowing someone with AD. Gender had no significant relationships with any of the outcome variables examined. Interpretations of these findings were discussed.

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Dalrymple-Alford, Joseph. "Does vocabulary knowledge influence speech recognition in adverse listening conditions?" Thesis, University of Canterbury. Communication Disorders, 2014. http://hdl.handle.net/10092/9334.

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Purpose: To investigate the effects of vocabulary, working memory, age, semantic context, and signal-to-noise-ratio (SNR) on speech recognition in adverse conditions (multitalker babble) in normal-hearing listeners aged 18-35. First, a general hypothesis was tested that listeners with larger receptive vocabularies would be more accurate at recognising speech in noise than listeners with more limited receptive vocabularies, even when target stimuli are words with high lexical frequency. A second more specific hypothesis was that the vocabulary would be predictive of speech recognition accuracy when the signal was moderately degraded, but not mildly or severely degraded. Method: 80 sentences with a high (HP) or low (LP) degree of semantic predictability (40 HP and 40 LP) were recorded from a male speaker of NZ English. These sentences were used as experimental target stimuli, and presented in multitalker babble at four SNRs: -8, -4, 0 and 4 dB SNR. Thirty-five participants (11 males and 24 females, aged 18 to 35), with puretone hearing thresholds of 15 dB HL or better, completed the Peabody Picture Vocabulary Test (PPVT) and the Wechsler Adult Intelligence Scale (WAIS) vocabulary subtest, the WAIS working memory subtests, and the experimental listening task in which they were required to repeat back the target sentences. Results: There was considerable variability between listeners in speech recognition performance, in terms of percent words accurately recognised overall (M = 45.8%; SD = 7.4) and for both HP (M = 54.4%; SD = 9.8) and LP (M = 35%; SD = 8.9) conditions. Hierarchical multiple regression analyses revealed that receptive (PPVT) and productive (WAIS) vocabulary knowledge, but not working memory, contributed 8 significant variance to listeners’ speech recognition scores overall and in both the HP and LP conditions. Further regression analyses at individual SNR levels showed that receptive vocabulary contributed significant variance to listening recognition scores in all predictability and SNR conditions except the most favourable (HP stimuli at 4 dB SNR) and least favourable (LP stimuli at -8 dB SNR) listening conditions. Working memory and age were not significantly related to overall listening score, HP listening score, or LP listening score, but age did contribute significant variance in the - 4dB SNR LP condition. Conclusion: The results provide further evidence that greater vocabulary knowledge is associated with improved speech recognition in adverse conditions. This effect was salient in mid-range adverse listening conditions, but was not apparent in highly favourable and extremely poor listening conditions. The results were interpreted to suggest that in moderately adverse listening conditions listeners with larger lexicons may be better able to exploit redundancies and/or intelligible ‘glimpses’ in the speech signal.

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Books on the topic "Knowledge differences"

1

Tirre, William C. Knowledge and ability factors underlying simple learning by accretion. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1989.

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Das verfluchte Geschlecht: Motive der Philosophie Otto Weiningers im Werk Georg Trakls. Frankfurt am Main: P. Lang, 1992.

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Yates, Lyn. Theory/practice dilemmas: Gender, knowledge and education. Geelong, Vic: Deakin University : distributed by Deakin University Press, 1990.

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Yi, Joseph. God and karate on the Southside: Bridging differences, building American communities. Lanham, MD: Lexington Books, 2009.

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Kusch, James. Knowledge, differences and identity in the time of globalization: Institutional discourse and practices. Newcastle upon Tyne: Cambridge Scholars, 2011.

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Reconcilable differences in eighteenth-century English literature. Newark: University of Delaware Press, 1999.

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Piekara, Frank H. Wie idiosynkratisch ist Wissen?: Individuelle Unterschiede im Assoziieren und bei der Anlage und Nutzung von Informationssystemen. Frankfurt am Main: P. Lang, 1988.

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Knowing and reasoning in college: Gender-related patterns in students' intellectual development. San Francisco: Jossey-Bass, 1992.

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Neurodiversity: Discovering the extraordinary gifts of autism, ADHD, dyslexia, and other brain differences. Cambridge, MA: Da Capo Lifelong, 2010.

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Armstrong, Thomas. Neurodiversity: Discovering the extraordinary gifts of autism, ADHD, dyslexia, and other brain differences. Cambridge, MA: Da Capo Lifelong, 2010.

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Book chapters on the topic "Knowledge differences"

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Bukowski, Henryk. "Self-Knowledge." In Encyclopedia of Personality and Individual Differences, 4771–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_2004.

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Bukowski, Henryk. "Self-Knowledge." In Encyclopedia of Personality and Individual Differences, 1–7. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-28099-8_2004-1.

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Gorelick, Root, Jessica Carpinone, and Lindsay Jackson Derraugh. "Fundamental Differences Between Females and Males?" In Crossroads of Knowledge, 9–22. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01979-6_2.

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Paez, D., and A. I. Vergara. "Culture Differences in Emotional Knowledge." In Everyday Conceptions of Emotion, 415–34. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8484-5_24.

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Spinillo, Alina G., and Chris Pratt. "Sociocultural Differences in Children’s Genre Knowledge." In Writing in Context(s), 27–48. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-24250-3_2.

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Foley, Mícheál, and Anna Hart. "Expert-Novice Differences and Knowledge Elicitation." In The Psychology of Expertise, 233–44. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9733-5_14.

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Budhathoki, Kailash, and Jilles Vreeken. "The Difference and the Norm — Characterising Similarities and Differences Between Databases." In Machine Learning and Knowledge Discovery in Databases, 206–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_13.

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Touraille, Priscille. "Human Sex Differences in Height: Evolution due to Gender Hierarchy?" In Crossroads of Knowledge, 65–75. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01979-6_7.

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Del Giudice, Manlio, Elias G. Carayannis, and Maria Rosaria Della Peruta. "Cultural Differences Across and Within Countries: Emerging Economies Matter." In Cross-Cultural Knowledge Management, 63–77. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-2089-7_6.

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Dwyer, Johanna T. "Dietary Standards and Guidelines: Similarities and Differences Among Countries." In Present Knowledge in Nutrition, 1110–34. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781119946045.ch65.

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Conference papers on the topic "Knowledge differences"

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Kuwabara, Megumi, and Linda B. Smith. "Cultural differences in relational knowledge." In 2009 IEEE 8th International Conference on Development and Learning. IEEE, 2009. http://dx.doi.org/10.1109/devlrn.2009.5175506.

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SCHROEDER, JAMES. "Adaptive tutors - Exploiting knowledge of individual differences." In 7th Computers in Aerospace Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-3031.

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Ntoutsi, Eirini, Kostas Stefanidis, Katharina Rausch, and Hans-Peter Kriegel. ""Strength Lies in Differences"." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2661829.2662026.

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Ueda, Natsuki, Katsumi Watanabe, and Kanji Tanaka. "Gender differences in visuomotor sequence learning." In 2016 8th International Conference on Knowledge and Smart Technology (KST). IEEE, 2016. http://dx.doi.org/10.1109/kst.2016.7440507.

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Whitelock-Wainwright, Alexander, Yi-Shan Tsai, Kayley Lyons, Svetlana Kaliff, Mike Bryant, Kris Ryan, and Dragan Gašević. "Disciplinary differences in blended learning design." In LAK '20: 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375462.3375488.

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Punchoojit, Lumpapun, and Thippaya Chintakovid. "Influence of age group differences on website cultural usability." In Knowledge Engineering 2011) - Conference postponed to 2012. IEEE, 2012. http://dx.doi.org/10.1109/ictke.2012.6152413.

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Krčál, Michal, and Michal Kubiš. "Differences between Knowledge and Information Management Practices: Empirical Investigation." In 8th International Conference on Knowledge Management and Information Sharing. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006053501900198.

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"Differences in Communication Skills Between Business, Economics and Management University Students." In 20th European Conference on Knowledge Management. ACPI, 2019. http://dx.doi.org/10.34190/km.19.167.

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Liu, Lan. "Skill Premium and Wage Differences: The Case of China." In 2009 Second International Symposium on Knowledge Acquisition and Modeling. IEEE, 2009. http://dx.doi.org/10.1109/kam.2009.239.

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Matsuyoshi, Daisuke, Kana Kuraguchi, Hiroshi Ashida, and Katsumi Watanabe. "Interhemispheric differences in the perception of human gaze direction." In 2014 6th International Conference on Knowledge and Smart Technology (KST). IEEE, 2014. http://dx.doi.org/10.1109/kst.2014.6775406.

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Reports on the topic "Knowledge differences"

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Taylor, Teresa Z. Developing Army Leaders Across Components: Assessing Knowledge Similarities and Differences. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada469719.

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Hanushek, Eric, Jens Ruhose, and Ludger Woessmann. Knowledge Capital and Aggregate Income Differences: Development Accounting for U.S. States. Cambridge, MA: National Bureau of Economic Research, June 2015. http://dx.doi.org/10.3386/w21295.

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Carretta, Thomas R., and Thomas W. Doub. Group Differences in the Role of g and Prior Job Knowledge in the Acquisition of Subsequent Job Knowledge. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada345014.

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Rudolph, Rodger J. Differences in TRICARE Knowledge from Department of Defense Active Duty Family Member Beneficiaries,. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada372372.

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Koubek, Richard J. The Role of Training, Individual Differences and Knowledge Representation in Cognitive-Oriented Task Performance. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada231798.

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Chetty, Raj, John Friedman, and Emmanuel Saez. Using Differences in Knowledge Across Neighborhoods to Uncover the Impacts of the EITC on Earnings. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18232.

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.

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Sullivan, Patrick K. The Difference that Makes a Difference: Distinguishing between Knowledge Management and Information Management in the U.S. Army. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada545232.

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Tiruneh, Dawit T., John Hoddinott, Caine Rolleston, Ricardo Sabates, and Tassew Woldehanna. Understanding Achievement in Numeracy Among Primary School Children in Ethiopia: Evidence from RISE Ethiopia Study. Research on Improving Systems of Education (RISE), May 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/071.

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Ethiopia has succeeded in rapidly expanding access to primary education over the past two decades. However, learning outcomes remain low among primary school children and particularly among girls and children from disadvantaged backgrounds. Starting with a systematic review of quantitative studies on the determinants of learning outcomes among primary school children in Ethiopia, this study then examined key determinants of students’ numeracy achievement over the 2018-19 school year. The study focused on Grade 4 children (N=3,353) who are part of an on-going longitudinal study. The two questions that guided this study are: what are the key determinants of numeracy achievement at Grade 4 in primary schools in Ethiopia, and how does our current empirical study contribute to understanding achievement differences in numeracy among primary school children in Ethiopia? We employed descriptive and inferential statistics to examine factors that determine differences in numeracy scores at the start and end of the school year, as well as determinants of numeracy scores at the end of the school year conditional on achievement at the start of the school year. We examined differences across gender, region, and rural-urban localities. We also used ordinary least squares and school ‘fixed effects’ approaches to estimate the key child, household and school characteristics that determine numeracy scores in Grade 4. The findings revealed that boys significantly outperformed girls in numeracy both at the start and end of the 2018/19 school year, but the progress in numeracy scores over the school year by boys was similar to that of girls. Besides, students in urban localities made a slightly higher progress in numeracy over the school year compared to their rural counterparts. Students from some regions (e.g., Oromia) demonstrated higher progress in numeracy over the school year relative to students in other regions (e.g., Addis Ababa). Key child (e.g., age, health, hours spent per day studying at home) and school- and teacher-related characteristics (e.g., provision of one textbook per subject for each student, urban-rural school location, and teachers’ mathematics content knowledge) were found to be significantly associated with student progress in numeracy test scores over the school year. These findings are discussed based on the reviewed evidence from the quantitative studies in Ethiopia.

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Filmer, Deon, Ezequiel Molina, and Waly Wane. Identifying Effective Teachers: Lessons from Four Classroom Observation Tools. Research on Improving Systems of Education (RISE), August 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/045.

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Four different classroom observation instruments—from the Service Delivery Indicators, the Stallings Observation System, the Classroom Assessment Scoring System, and the Teach classroom observation instrument—were implemented in about 100 schools across four regions of Tanzania. The research design is such that various combinations of tools were administered to various combinations of teachers, so these data can be used to explore the commonalities and differences in the behaviors and practices captured by each tool, the internal properties of the tools (for example, how stable they are across enumerators, or how various indicators relate to one another), and how variables collected by the various tools compare to each other. Analysis shows that inter-rater reliability can be low, especially for some of the subjective ratings; principal components analysis suggests that lower-level constructs do not map neatly to predetermined higher-level ones and suggest that the data have only a few dimensions. Measures collected during teacher observations are associated with student test scores, but patterns differ for teachers with lower versus higher subject content knowledge.

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