On the Teachability of Randomized Learners

The present paper introduces a new model for teaching {em randomized learners}. Our new model, though based on the classical teaching dimension model, allows to study the influence of various parameters such as the learner\u27s memory size, its ability to provide or to not provide feedback, and the influence of the order in which examples are presented. Furthermore, within the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Furthermore, we provide characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback

The teaching size: computable teachers and learners for universal languages

[EN] The theoretical hardness of machine teaching has usually been analyzed for a range of concept languages under several variants of the teaching dimension: the minimum number of examples that a teacher needs to figure out so that the learner identifies the concept. However, for languages where concepts have structure (and hence size), such as Turing-complete languages, a low teaching dimension can be achieved at the cost of using very large examples, which are hard to process by the learner. In this paper we introduce the teaching size, a more intuitive way of assessing the theoretical feasibility of teaching concepts for structured languages. In the most general case of universal languages, we show that focusing on the total size of a witness set rather than its cardinality, we can teach all total functions that are computable within some fixed time bound. We complement the theoretical results with a range of experimental results on a simple Turing-complete language, showing how teaching dimension and teaching size differ in practice. Quite remarkably, we found that witness sets are usually smaller than the programs they identify, which is an illuminating justification of why machine teaching from examples makes sense at all.We would like to thank the anonymous referees for their helpful comments. This work was supported by the EU (FEDER) and the Spanish MINECO under grant RTI2018-094403-B-C32, and the Generalitat Valenciana PROMETEO/2019/098. This work was done while the first author visited Universitat Politecnica de Valencia and also while the third author visited University of Bergen (covered by Generalitat Valenciana BEST/2018/027 and University of Bergen). J. Hernandez-Orallo is also funded by an FLI grant RFP2-152.Telle, JA.; Hernández-Orallo, J.; Ferri Ramírez, C. (2019). The teaching size: computable teachers and learners for universal languages. Machine Learning. 108(8-9):1653-1675. https://doi.org/10.1007/s10994-019-05821-2S165316751088-9Angluin, D., & Kriķis, M. (2003). Learning from different teachers. Machine Learning, 51(2), 137–163.Balbach, F. J. (2007). Models for algorithmic teaching. Ph.D. thesis, University of Lübeck.Balbach, F. J. (2008). Measuring teachability using variants of the teaching dimension. Theoretical Computer Science, 397(1–3), 94–113.Balbach, F. J., & Zeugmann, T. (2009). Recent developments in algorithmic teaching. In Intl conf on language and automata theory and applications (pp. 1–18). Springer.Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning (pp. 41–48). ACM.Biran, O., & Cotton, C. (2017). Explanation and justification in machine learning: A survey. In IJCAI-17 Workshop on explainable AI (XAI) (p. 8).Böhm, C. (1964). On a family of turing machines and the related programming language. ICC Bulletin, 3(3), 187–194.Elias, P. (1975). Universal codeword sets and representations of the integers. IEEE Transactions on Information Theory, 21(2), 194–203.Freivalds, R., Kinber, E. B., & Wiehagen, R. (1989). Inductive inference from good examples. In International workshop on analogical and inductive inference (pp. 1–17). Springer.Freivalds, R., Kinber, E. B., & Wiehagen, R. (1993). On the power of inductive inference from good examples. Theoretical Computer Science, 110(1), 131–144.Gao, Z., Ries, C., Simon, H. U., & Zilles, S. (2016). Preference-based teaching. In Conf. on learning theory (pp. 971–997).Gold, E. M. (1967). Language identification in the limit. Information and Control, 10(5), 447–474.Goldman, S. A., & Kearns, M. J. (1995). On the complexity of teaching. Journal of Computer and System Sciences, 50(1), 20–31.Goldman, S. A., & Mathias, H. D. (1993). Teaching a smart learner. In Conf. on computational learning theory (pp. 67–76).Gulwani, S., Hernández-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U., & Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM, 58(11).Hernandez-Orallo, J., & Telle, J. A. (2018). Finite biased teaching with infinite concept classes. arXiv preprint. arXiv:1804.07121 .Jun, S. W. (2016). 50,000,000,000 instructions per second: Design and implementation of a 256-core brainfuck computer. Computer Science and AI Laboratory, MIT.Khan, F., Mutlu, B., & Zhu, X. (2011). How do humans teach: On curriculum learning and teaching dimension. In Advances in neural information processing systems (pp. 1449–1457).Lake, B., & Baroni, M. (2018). Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. In ICML (pp. 2879–2888).Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.Lázaro-Gredilla, M., Lin, D., Guntupalli, J. S., & George, D. (2019). Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. Science Robotics 4.Levin, L. A. (1973). Universal Search Problems. Problems of Information Transmission, 9, 265–266.Li, M., & Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications (3rd ed.). New York, NY: Springer.Lieberman, H. (2001). Your wish is my command: Programming by example. San Francisco, CA: Morgan Kaufmann.Shafto, P., Goodman, N. D., & Griffiths, T. L. (2014). A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology, 71, 55–89.Shinohara, A., & Miyano, S. (1991). Teachability in computational learning. New Generation Computing, 8(4), 337–347.Simard, P. Y., Amershi, S., Chickering, D. M., Pelton, A. E., Ghorashi, S., Meek, C., Ramos, G., Suh, J., Verwey, J., & Wang, M., et al. (2017). Machine teaching: A new paradigm for building machine learning systems. arXiv preprint arXiv:1707.06742 .Solomonoff, R. J. (1964). A formal theory of inductive inference. Part I. Information and Control, 7(1), 1–22.Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134–1142.Vapnik, V. N., & Chervonenkis, A. Y. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and Its Applications, 16, 264–280.Zhu, X. (2013). Machine teaching for Bayesian learners in the exponential family. In Neural information processing systems 26, Curran (pp. 1905–1913).Zhu, X. (2015). Machine teaching: An inverse problem to machine learning and an approach toward optimal education. In AAAI (pp. 4083–4087).Zhu, X., Singla, A., Zilles, S., & Rafferty, A. N. (2018). An overview of machine teaching. arXiv preprint arXiv:1801.05927

Celebrations 2023

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Bridging the Divide Between Upper and Lower Classmen: Resources for Mentors and Freshmen in the Choral Education Program

The recent decline in student retention in the choral education program at Utah State University should be cause for concern. Students who are excited and passionate about choir are leaving choral education and/or dropping out of college entirely. However, research into college retention rates and the experiences of choral education students at USU has helped explain this decline in student retention. My review of research about college retention rates indicates that some of the biggest reasons that students drop out of college are academic difficulty, interpersonal difficulties, and mental health. It also indicates students with a strong sense of support from teachers and friends at their college have a greater likelihood of graduating. The research also indicates that identifying “at-risk” students and directing university resources towards those students has a high chance of increasing student retention between their freshmen and sophomore year. My research also included interviewing previous and current choral education students from USU about their experience in the program. After carefully reviewing their answers and suggestions, it is clear that this choral education program is missing key aspects of student support that promote retention. At the end of this academic review, I have outlined three major changes to the program that will likely increase student retention by providing new resources to students through USU’s chapter of The American Choral Directors Association (ACDA)

A Descriptive Qualitative Study Exploring Middle-School Teachers’ Perceptions of Professional Development on Technology Integration

Today’s teachers are being encouraged to incorporate technology into their classrooms. Technology integration became a worldwide focus for schools after remote learning was necessary to continue instruction due to the COVID-19 pandemic. Additionally, research shows that technology-infused lessons improve student achievement and increase student engagement. Despite efforts to support teachers throughout the technology integration process, concerns have developed. Preparing highly qualified teachers ready to incorporate technology into their teaching repertoire has developed additional stress factors. In this descriptive qualitative study, the researcher wanted to address the problem of teacher attrition, possibly related to stress factors associated with technology integration. The purpose of this qualitative descriptive study was to explore teachers’ perceptions of professional development opportunities that possibly improve the technology integration process. Additionally, the researcher wanted to identify stress factors associated with technology adoption and how professional development may help to reduce stress factors associated with technology integration in one middle school in New York. The researcher chose a qualitative descriptive study using Vygotsky’s social constructivist theory and Bandura’s social learning theory on self-efficacy as the theoretical framework. The researcher included an exposition of the literature sources, synthesized the research findings, and provided recommendations for practice and future research. The data collection process consisted of semistructured open-ended questions that were developed with the support of a panel of experts. There were 10 participants chosen using a snowball sampling strategy. This study’s findings were that professional development should be hands-on, continuous, and targeted to increase teachers’ personal level of engagement. Also, creating opportunities for colleague support systems reduced stress factors associated with technology integration. These peer support systems reduced the time required to research the most effective resources, digital tools, and applications as participants shared the resources with one another. Recommendations for practice included providing adequate professional development, offering appropriate infrastructure, and hands-on, targeted, continuous training for teachers to feel more comfortable developing technology-infused lessons. Recommendations for research include providing additional insight into teachers’ perceived benefits and motivation for technology integration and how stress factors associated with the technology adoption process possibly increase teacher attrition