Abstract
In any teaching and learning system assessment is an integral part. In any conventional evaluation process assessment is performed by human instructors or Subject Matter Experts (SMEs). In context to Industry 4.0 Authentic Learning plays a significant role, as well it pursues important role in academia. In case of automated assessment, the system requires classified items of various level of cognitive domain so that thorough assessment can be performed. In the proposed methodology, the authors use pre-classified subjective type items from the Item Bank. Each item belongs to a particular level of cognitive domain with a weighted value assigned to it, these weights related to individual items are predefined by the instructors/SME based on level of mastery in that subject domain along with appropriate level of cognitive domain. In this research, the authors proposed a system where the subjective type of assessment is done based on the pre-classified items and the same using various Deep Learning (DL) and Machine Learning (ML) techniques. Randomly chosen pre-classified items from the Item Bank are selected, which are evenly distributed from the various levels of the cognitive domain of Bloom’s Taxonomy. Each obtained response is traversed through a pre-processing module. To extract the features, tokenization is performed on the obtained pre-processed response. The extracted features will be presented in the numerical format and is the input for the regression module. The regression module provides classified responses for each item in the given evaluation. A training set (validation set) which is already available for the evaluation process will compare with the classified responses. Using the above-mentioned workflow based on the metrics like accuracy the performance of a specific learner is measured.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Donovan, M.S., Bransford, J.D., Pellegrino, J.W. (eds.): How People Learn: Bridging Research and Practice. National Academy Press, Washington, DC (1999)
Bloom, B.S.: Taxonomy of Educational Objectives: The Classification of Educational Goals- (Part1&2). D. McKay, New York (1956)
https://developers.google.com/machine-learning/guides/text-classification. Accessed 1 June 2022
Marton, F., Saljo, R.: On qualitative difference in learning: I-outcome and process. Br. J. Educ. Psychol. 46(1), 4–11 (1976)
Swart, A.J.: Evaluation of final examination papers in engineering: a case study using Bloom’s taxonomy. IEEE Trans. Educ. 53(2), 257–264 (2010)
Kar, S.P., Mandal, J.K., Chatterjee, R.: A comprehension based intelligent assessment architecture. In: 6th IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE-2017), pp. 368–371. IEEE, Hong Kong, China (2017)
Kar, S.P., Chatterjee, R., Mandal, J.K.: Intelligent assessment using variable N-gram technique. In: 22nd International Conference on Interactive Collaborative Learning, (ICL -2019), pp. 846–854, Bangkok, Thailand (2019)
Chatterjee, R., Mandal, J.K.: A Content Proposer System (CPS) based on deficiencies of confidence based learning (CBL). J. Sci. Ind. Res. 78, 35–38 (2019)
Jeyashree, S., Ravichandran, R.: Web impact assessment of identified higher education institutions in India. Ann. Libr. Inf. Stud. 62, 7–18 (2015)
Karaci, A.: Fuzzy logic based assessment model proposal for online problem based learning. Int. J. Comput. Appl. 117, 5–8 (2015)
Khalid, A., Hani, H.: An adaptive fuzzy logic based system for improved knowledge delivery within intelligent e-learning platforms (2013)
Omar, N., et al.: Automated analysis of exam questions according to bloom’s taxonomy, UKM teaching and learning congress. Procedia Soc. Behav. Sci. 59, 297–303 (2012)
Jayakodi, K., Bandara, M., Perera, I., Meedeniya, D.: WordNet and Cosine similarity based classifier of exam questions using Bloom’s taxonomy. iJET 11(4), 142–149 (2016)
Diab, S., Sartawi, B.: Classification of questions and Learning Outcome Statements (LOS) Into Blooms Taxonomy (BT) by similarity measurements towards extracting of learning outcome from learning material. Int. J. Managing Inf. Technol. (IJMIT) 9(2), 1–12 (2017)
von Konsky, B.R., Zheng, L., Parkin, E., Huband, S., Gibson, D.: Parts of speech in Bloom’s taxonomy classification. In: Campbell, M. et al. (eds.) Open Oceans: Learning without borders. Proceedings ASCILITE 2018 Geelong, pp. 527–532 (2018)
Das, S., Das Mandal, S.K., Basu, A.: Identification of cognitive learning complexity of assessment questions using multi-class text classification. Contemp. Educ. Technol. 12(2), ep275 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kar, S.P., Chatterjee, R., Mandal, J.K. (2023). Automated Assessment - An Application in Authentic Learning Using Bloom’s Taxonomy. In: Auer, M.E., Pachatz, W., Rüütmann, T. (eds) Learning in the Age of Digital and Green Transition. ICL 2022. Lecture Notes in Networks and Systems, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-031-26190-9_78
Download citation
DOI: https://doi.org/10.1007/978-3-031-26190-9_78
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26189-3
Online ISBN: 978-3-031-26190-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)