Prediction of Student&amp;apos;s Performance based on Incremental Learning

Kulkarni, Pallavi; Ade, Roshani

doi:10.5120/17440-8211

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…Online incremental learning is an Artificial intelligence (AI) technique that refers to the circumstance of a permanent online adaptation of the analytical model according to the constantly received data flow over time [4,5,13]. This technique has been used to fulfill adequately various learning analytics objectives among which predicting students performance [1,6,7], image classification [10,14] and text classification [11]. Most of the existing works focus either on solving the problem of catastrophic forgetting and concept drift, or on comparing incremental online algorithms.…”

Section: Related Workmentioning

confidence: 99%

When and How to Update Online Analytical Models for Predicting Students Performance?

Labba

Boyer

2022

Lecture Notes in Computer Science

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One of the main concerns in online learning environments is the identification of students with learning difficulties. Conventionally, analytical models trained offline on pre-prepared datasets are used to predict student performance. However, as learning data become progressively available over time, this learning method is no longer sufficient in real-world applications. Nowadays, incremental learning strategies are increasingly applied to update online analytical models by re-training them on newly received data. Various online incremental learning approaches have been proposed to overcome different issues such as catastrophic forgetting and concept drift. However, no approach addresses the question of when to update the model and how to determine whether the new data provide important information that the model should learn. In this paper, we propose a method for determining when an online classifier that predicts student performance and receives a real-time data stream, should be updated. In addition, we use a typical approach that maintains balanced old and new data examples to re-train the model when necessary. As a proof of concept, we applied our method on real data of k-12 learners enrolled in an online physics-chemistry module.

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Section: Related Workmentioning

confidence: 99%

When and How to Update Online Analytical Models for Predicting Students Performance?

Labba

Boyer

2022

Lecture Notes in Computer Science

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“…After (Salzberg, 1991;Wettschereck & Dietterich, 1995), modifications to the NGE theory were presented in (Martin, 1995), modifications to the classification algorithms were proposed in (Zaharie et al, 2011) and implementations of these classification algorithms for applications such as clustering or predicting student's performance are explored in (Hamidzadeh et al, 2015) and (Kulkarni & Ade, 2014), respectively. Nevertheless, no other modifications to the attribute weight learning algorithms of (Salzberg, 1991;Wettschereck & Dietterich, 1995) have been proposed.…”

Section: Attribute Weight Learningmentioning

confidence: 99%

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Torrent-Fontbona

Meléndez

López

2019

Expert Systems with Applications

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People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system's response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.

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“…The simplest way to deal with such lost data is to neglect those instances that have missing attributes. When large portions of the data is facing problem of missing features or lost attributes filtering or deletion (list wise) are suboptimal (and impractical due to conditions applied on them to work well) approaches and generally known as filtering [85]. A more realistic approach is imputation where missing values are filled with a meaningful calculation [86][87][88][89] [84] produces sufficient frequency of classifiers whereby each one is trained with randomly chosen feature subset.…”

Section: Issues Of Data Stream Learning -Missing Featuresmentioning

confidence: 99%

Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review

Kulkarni¹,

Ade²

2014

IJDKP

Self Cite

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Recently, stream data mining applications has drawn vital attention from several research communities. Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine learning area has been developing learning algorithms that have certain assumptions on underlying distribution of data such as data should have predetermined distribution. Such constraints on the problem domain lead the way for development of smart learning algorithms performance is theoretically verifiable. Real-word situations are different than this restricted model. Applications usually suffers from problems such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also usual in real world applications, resulting in the "concept drift" which is related with data stream examples. These issues have been separately addressed by the researchers, also, it is observed that joint problem of class imbalance and concept drift has got relatively little research. If the final objective of clever machine learning techniques is to be able to address a broad spectrum of real world applications, then the necessity for a universal framework for learning from and tailoring (adapting) to, environment where drift in concepts may occur and unbalanced data distribution is present can be hardly exaggerated. In this paper, we first present an overview of issues that are observed in stream data mining scenarios, followed by a complete review of recent research in dealing with each of the issue.

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Prediction of Student's Performance based on Incremental Learning

Cited by 10 publications

References 33 publications

When and How to Update Online Analytical Models for Predicting Students Performance?

When and How to Update Online Analytical Models for Predicting Students Performance?

Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes

Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review

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