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003			EG-GiCUC
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008			210329s2021 ua dh f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.20.03.Ph.D.2021.Mo.E
100	0		_aMohamed Abdellatif Hussein Mohamed
245	1	0	_aExtended constructed response questions scoring with adaptive feedback / _cMohamed Abdellatif Hussein Mohamed ; Supervised Hesham Ahmed Hassan , Mohammed Nassef Fatouh
246	1	5	_aتصحيح آلى لأسئلة إنتاج الاستجابة المطولة مع تقديم تغذية راجعة تكيفية
260			_aCairo : _bMohamed Abdellatif Hussein Mohamed , _c2021
300			_a114 Leaves : _bcharts , facsimiles ; _c30cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science
520			_aOver the past years, there are many Automated Essay Scoring (AES) systems that have been created based on Artificial Intelligence (AI) models. The improvement in deep learning has demonstrated that applying neural network approaches to AES systems has achieved state-of-the-art solutions. Most neural-based AES systems would allocate an overall score or mark to essays, even if they scored by using analytical scoring rubrics. The scoring of each trait in analytical rubrics helps to detect learners' levels of performance. Additionally, offering adaptive feedback to each learner about his/her writing is a vital component of assessing the performance. Constructing adaptive feedback to each learner empowers the identification of the learner's strengths and weaknesses. It also helps in improving learner's future writings. In this thesis, a framework has been built up to reinforce the validity of the scoring process and increase the reliability of a baseline neural-based AES model by evaluating the writing traits in addition to the overall writing. The model has been extended based on the prediction of the traits' scores to deliver trait-specific adaptive feedback. Multiple deep learning models of the automatic scoring were explored, and several analyses took place to come up with some indicators from these models. The findings of the experiments demonstrate that Long Short-Term Memory (LSTM) based system beat the baseline study by 4.6% in terms of the Quadratic Weighted Kappa (QWK). Likewise, the prediction of the traits' scores improves the efficacy of the prediction of the overall essay score. It is also found that the LSTM model is the best model to predict scores for essays that include relatively long sequences of words, which is consistent with the nature of the LSTM models. It is also found that the clarity of the scoring rubrics influences the accuracy of both human and the proposed model (AESAUG) scores
530			_aIssued also as CD
653		4	_aAdaptive feedback
653		4	_aAES System
653		4	_aTrait evaluation
700	0		_aHesham Ahmed Hassan , _eSupervisor
700	0		_aMohammed Nassef Fatouh , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c80442 _d80442