Cross-linguistic Influence in Structural Errors in EFL Writing / Nour El Houda Djekidel

Public ISBD Titre : Cross-linguistic Influence in Structural Errors in EFL Writing : A Case Study of M1 Learners at the University of Amar Thelidji, Laghouat Type de document : texte manuscrit Auteurs : Nour El Houda Djekidel, Auteur ; Mhamedi, Nadir, Directeur de thèse Editeur : Laghouat : University Amar Telidji - Department of English Année de publication : 2025 Importance : 68 p. Accompagnement : CD Langues : Anglais (eng) Catégories : THESES :20 Langue et litterature anglaise Mots-clés : cross-linguistic influence, contrastive analysis hypothesis, error analysis, Arab EFL learners, structural errors Résumé : This research examines the structural errors observed in M1 EFL learners’ writings at the University of Amar Thelidji in Laghouat, with special emphasis on the learners’ first language influence, Standard Arabic, on their written production. This study aims at identifying the common structural deviations, including grammatical and syntactic errors, and studying their possible sources. A corpus-based design was implemented to collect and analyse learners’ essays besides Rod Ellis’ (2008) framework. In addition, two analytical procedures were also applied to ensure systematic and comprehensible findings. These procedures are contrastive analysis and error analysis. Eleven categories were identified, including six grammatical, such as subject-verb agreement, article use and verb formation. Besides, five syntactic categories, such as word order, question order and parallel structure issues. The findings revealed that the majority of errors stemmed from intralingual factors such as overgeneralisation and incomplete rule application. Interlingual errors, on the other hand, were primarily observed in article use, verb formation and prepositional use; these errors often appear due to negative transfer between Standard Arabic and English. Thus, intralingual errors were more frequent than interlingual errors. The results of this study are important for English language teaching, especially for Arabic-speaking learners. It highlights the importance of teaching contrastive grammar that is targeted for Arab EFL learners to increase their awareness of the structural differences between Standard Arabic and English note de thèses : Degree in English Language Teaching (ELT) Cross-linguistic Influence in Structural Errors in EFL Writing : A Case Study of M1 Learners at the University of Amar Thelidji, Laghouat [texte manuscrit] / Nour El Houda Djekidel, Auteur ; Mhamedi, Nadir, Directeur de thèse . - Laghouat : University Amar Telidji - Department of English, 2025 . - 68 p. + CD. Langues : Anglais (eng) Catégories : THESES :20 Langue et litterature anglaise Mots-clés : cross-linguistic influence, contrastive analysis hypothesis, error analysis, Arab EFL learners, structural errors Résumé : This research examines the structural errors observed in M1 EFL learners’ writings at the University of Amar Thelidji in Laghouat, with special emphasis on the learners’ first language influence, Standard Arabic, on their written production. This study aims at identifying the common structural deviations, including grammatical and syntactic errors, and studying their possible sources. A corpus-based design was implemented to collect and analyse learners’ essays besides Rod Ellis’ (2008) framework. In addition, two analytical procedures were also applied to ensure systematic and comprehensible findings. These procedures are contrastive analysis and error analysis. Eleven categories were identified, including six grammatical, such as subject-verb agreement, article use and verb formation. Besides, five syntactic categories, such as word order, question order and parallel structure issues. The findings revealed that the majority of errors stemmed from intralingual factors such as overgeneralisation and incomplete rule application. Interlingual errors, on the other hand, were primarily observed in article use, verb formation and prepositional use; these errors often appear due to negative transfer between Standard Arabic and English. Thus, intralingual errors were more frequent than interlingual errors. The results of this study are important for English language teaching, especially for Arabic-speaking learners. It highlights the importance of teaching contrastive grammar that is targeted for Arab EFL learners to increase their awareness of the structural differences between Standard Arabic and English note de thèses : Degree in English Language Teaching (ELT)

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
Thlg 20.541	Thlg 20.541	CD	BIBLIOTHEQUE DES LITTERATURES ET LANGUES	Lettres et langue anglaises (bll)	Disponible

IOT based smart irrigation management system / Nour El Houda Djekidel

Public ISBD Titre : IOT based smart irrigation management system Type de document : document multimédia Auteurs : Nour El Houda Djekidel, Auteur ; Fatima Zahra Lahreche, Auteur ; Lakhdar Kamel Oulad Djedid, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2026 Importance : 65 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Networks, distributed systems and applications Langues : Anglais (eng) Mots-clés : Smart irrigation  AIoT  XGBoost  Hybrid Edge–Cloud  Concept drift  Precision agriculture  Water management Résumé : The agricultural sector faces increasing challenges due to water scarcity and the inefficiency of traditional irrigation systems, which rely on fixed thresholds rather than accurate environmental data, leading to water wastage and suboptimal crop yields. To address these issues, this study proposes an adaptive hybrid Edge--Cloud AIoT smart irrigation system integrating ESP32-based IoT sensors with ML real-time irrigation decisions. A comparative evaluation of six classification models was conducted, with XGBoost identified as the optimal model achieving an F1 score of 0.896 (∼89.7%) prediction accuracy across three irrigation demand classes. The system features a dynamic routing mechanism with an adaptive confidence threshold (automatically updated from a sliding window of the last 20 predictions using mean and standard deviation statistics) that assigns high-confidence tasks to a lightweight edge rule engine while escalating complex cases to the cloud-based model. The system processes approximately 26--30% of requests locally at the edge layer, reducing cloud dependency and inference latency. An adaptive concept drift detection module further triggers autonomous background retraining to maintain long-term system robustness. note de thèses : Mémoire de master en informatique IOT based smart irrigation management system [document multimédia] / Nour El Houda Djekidel, Auteur ; Fatima Zahra Lahreche, Auteur ; Lakhdar Kamel Oulad Djedid, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2026 . - 65 p. + 1 disque optique numérique (CD-ROM). Option : Networks, distributed systems and applications Langues : Anglais (eng) Mots-clés : Smart irrigation  AIoT  XGBoost  Hybrid Edge–Cloud  Concept drift  Precision agriculture  Water management Résumé : The agricultural sector faces increasing challenges due to water scarcity and the inefficiency of traditional irrigation systems, which rely on fixed thresholds rather than accurate environmental data, leading to water wastage and suboptimal crop yields. To address these issues, this study proposes an adaptive hybrid Edge--Cloud AIoT smart irrigation system integrating ESP32-based IoT sensors with ML real-time irrigation decisions. A comparative evaluation of six classification models was conducted, with XGBoost identified as the optimal model achieving an F1 score of 0.896 (∼89.7%) prediction accuracy across three irrigation demand classes. The system features a dynamic routing mechanism with an adaptive confidence threshold (automatically updated from a sliding window of the last 20 predictions using mean and standard deviation statistics) that assigns high-confidence tasks to a lightweight edge rule engine while escalating complex cases to the cloud-based model. The system processes approximately 26--30% of requests locally at the edge layer, reducing cloud dependency and inference latency. An adaptive concept drift detection module further triggers autonomous background retraining to maintain long-term system robustness. note de thèses : Mémoire de master en informatique

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