Student Performance Prediction using Multi-Layers Artificial Neural Networks

Altaf, Saud; Soomro, Waseem; Rawi, Mohd Izani Mohamed

doi:10.1145/3325917.3325919

Cited by 27 publications

(18 citation statements)

References 8 publications

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“…The aim of Altaf et al (2019) research was to assess whether neural networks can be used to predict student performance based on data from a Campus Management System (CMS) log file. The applicability of neural networks was assessed through two case studies, comparing the predictive performance in the data set obtained from the log records of 900 students divided into 10 courses, between the years 2016 and 2017, that used the virtual Moodle environment.…”

Section: Resultsmentioning

confidence: 99%

“…In the third moment, find the activities and events that are strongly related to excellent students and finally, find the appropriate ML algorithms to predict inactive and underperforming students. Altaf et al (2019) used artificial neural networks to predict student performance based on log data from a campus management system, containing information on 900 students in 10 courses, thus allowing to compare predictive performance between courses and assess whether predictors that identify individual courses affect performance. Omar and Abdesselam (2017) used classification algorithms in the interaction traits with an e-learning platform and, specifically, in a log file extracted from the e-learning platform of the University of Bechar in order to test and compare the performance of the algorithms.…”

Section: Fq1 -Are There Methods/techniques Of Analysis That Have Been Using Historical Log Records Of Students In the Field Of Distance Ementioning

confidence: 99%

“…Table 7 List of articles that contain analysis methods using records and log history References/Authors Analysis Methods/Techniques Altaf et al (2019) Neural networks to predict student performance based on log data. Chanaa and Faddouli (2018) Customized model with three main components, sentimental analysis, cognitive analysis and learning style.…”

Section: Fq1 -Are There Methods/techniques Of Analysis That Have Been Using Historical Log Records Of Students In the Field Of Distance Ementioning

confidence: 99%

“…The benefits can be categorized into two aspects: 1) methods and techniques and 2) intelligent services. The benefits categorized as methods and techniques are: software frameworks for developing prediction models (Olivé et al, 2018), educational data mining system (Lara et al, 2014), pipeline infrastructure (Chaffai et al, 2017), student performance prediction structure (Qu et al, 2018), system oriented to analysis of results (Zorrilla et al, 2010), algorithms (Ros et al, 2017), methods based on a latent statistical class (Sorour et al, 2015), application of data mining using computational techniques (Peñafiel et al, 2018), mixed chart of terms (Clarizia et al, 2018), opinion mining platform (Spatiotis et al, 2018), data mining techniques such as grouping and association (Islam et al, 2019), statistical machine learning technique (Hussain et al, 2018), distributed feedback analysis engine for Big Data (Altaf et al, 2019), neural networks to predict performance (Olanrewaju et al, 2016), learning style prediction method, system that uses (Yang et al, 2014), voting methodology (Kotsiantis et al, 2010), based learning behavioral model (Chang and Chu, 2010), clustering algorithms (Omar and Abdesselam, 2017), method for predicting students' final grades by an RNR (Okubo et al, 2017), academic analysis tool (Graf et al, 2011), complementary techniques -agglomerate hierarchical cluster, epistemic network analysis and process mining (Rayón et al, 2014), learning analysis methodology (Uzir et al, 2020), method based on Learning Analytics (Guo et al, 2020) and explanatory model of student behavior (Jovanović et al, 2020).…”

Section: Gq2 -What Benefits Have Been Obtained For Students Teachers and Managersmentioning

confidence: 99%

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Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Silva¹,

Dias²,

Barbosa³

et al. 2021

Informatics in Education

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Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

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Section: Resultsmentioning

confidence: 99%

Section: Fq1 -Are There Methods/techniques Of Analysis That Have Been Using Historical Log Records Of Students In the Field Of Distance Ementioning

confidence: 99%

Section: Fq1 -Are There Methods/techniques Of Analysis That Have Been Using Historical Log Records Of Students In the Field Of Distance Ementioning

confidence: 99%

Section: Gq2 -What Benefits Have Been Obtained For Students Teachers and Managersmentioning

confidence: 99%

See 2 more Smart Citations

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Silva¹,

Dias²,

Barbosa³

et al. 2021

Informatics in Education

View full text Add to dashboard Cite

show abstract

“…Fruits and vegetables are highly perishable commodities, so proper post-harvest handling is required to avoid unwanted losses and to retain the freshness and quality. During long-distance transportation and distribution, the risk of post-harvest losses may increase, and therefore, proper care and handling have been emphasized in recent years for post-harvest commodities [ 2 ]. There are several causes of post-harvest losses, including increased respiration rate, hormone production (i.e., ethylene), physiological disorders, general senescence, and compositional and morphological changes.…”

Section: Introductionmentioning

confidence: 99%

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

Altaf

Ahmad

Zaindin

et al. 2020

Sensors

Self Cite

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Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

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A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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Student Performance Prediction using Multi-Layers Artificial Neural Networks

Cited by 27 publications

References 8 publications

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Learning analytics and collaborative groups of learners in distance education: a systematic mapping study

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

A survey on educational data mining methods used for predicting students' performance

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