Discovering and Recognizing Student Interaction Patterns in Exploratory Learning Environments

Bernardini, Andrea; Conati, Cristina

doi:10.1007/978-3-642-13388-6_17

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…Academic experiences and faculty preparedness affect directly campus services do not affect significantly [53]. Students' experience of acceptance influences multiple dimensions of their behavior but that schools adopt organizational practices that neglect and may actually undermine students' experience of membership in a supportive community [54]. It is often hard to identify actions or behaviors as correct or faulty, making it hard to provide an adaptive support to students who do not learn well with these environments [55].…”

Section: Learning Environmentmentioning

confidence: 99%

Graduate School Services: Basis for an Improvement Plan

Absolor¹,

Ferrer²,

N.³

2022

Proceedings of the 2nd International Conference on Education and Technology (ICETECH 2021)

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The study aimed to determine the students' level of satisfaction on the services of the Graduate School of Ilocos Sur Polytechnic State College along professional services, school facilities, and learning environment. It further determined the profile of the respondents in terms of age, sex, civil status, position, employment status, number of years in teaching and type of school employed in. The profile was correlated to their level of satisfaction. The weaknesses of the services were identified as basis in the development of an Improvement Plan. The study is a descriptive research employing correlational and developmental methods. A sample size of 120 graduate students were selected randomly with a questionnaire as an instrument in obtaining data that were treated using frequency count and percentage, simple bi-variate correlation, and Cronbach Alpha. Results show that the profile of the respondents are of varying background. The three services considered in the study obtained a high satisfaction level. Years of teaching is a determinant of satisfaction on professional services while position shows significant effect on their satisfaction of school facilities and learning environment. The accessibility of books and computers in the library and dormitories were identified as areas that require improvement.

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Section: Learning Environmentmentioning

confidence: 99%

Graduate School Services: Basis for an Improvement Plan

Absolor¹,

Ferrer²,

N.³

2022

Proceedings of the 2nd International Conference on Education and Technology (ICETECH 2021)

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“…& Pal, 2011), (Lin et al, 2013), (Bhise et al, 2013), (Jha & Ragha, 2013), (Chalaris et al, 2014), (Belsis et al, 2014), (Mayilvaganan & Kalpanadevi, 2015) Students patterns Identification (Sanjeev & Zytkow, 1995), (Mor & Minguillón, 2004), (Kay et al, 2006), (Ranjan & Khalil, 2008), (Chanchary, 2008b), (Pechenizkiy et al, 2008), (C. M. , (Bian, 2010), (Cristóbal Romero, Romero, et al, 2010), (Y. Zhang et al, 2010), (Bhargava et al, 2010), (Zhou, 2010), (Cristóbal Romero, Ventura, et al, 2010), (Bernardini & Conati, 2010), (Cobo et al, 2011), , , (Parack et al, 2012), (Varun Kumar & Chadha, 2012), (Agarwal et al, 2012), (Martinez-maldonado et al, 2013), (Mugla, 2014), (Campagni et al, 2015) Students related Prediction (Weibelzahl et al, 2007), (C. M. Chen & Chen, 2009), , (Fausett & Elwasif, 1994), (Gedeon & Turner, 1993), (T. , (Oladokun et al, 2008), (Hien & Haddawy, 2007), ( ZA Pardos & Heffernan, 2007), (Ayers & Junker, 2006), (Thai-nghe et al, 2010), (Z. A.…”

Section: Domainsmentioning

confidence: 99%

Data mining techniques applied in educational environments: Literature Review

Manjarres

Sandoval

Suárez

2018

DER

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Data Mining for Education is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students better. In recent years there have been various works related with this specialty and have used multiple data mining techniques derived from this to address different educational problems. The aim of this publication is to present a review of the works in which data mining techniques were used to solve specific problems of education and to do a classification of these, associated with diverse scenarios in which it has been applied.Resumen: La minería de datos para la educación es una disciplina emergente la cual busca el desarrollo de métodos que permitan explorar grandes cantidades de datos provenientes de entornos educativos con el fin de entender mejor a los estudiantes. En los últimos años se han realizado diversos trabajos relacionados con esta disciplina y se han utilizado múltiples técnicas de minería de datos para abordar diferentes problemáticas educativas. El objetivo de esta publicación es presentar una revisión de los trabajos en los cuales se han utilizado técnicas de minerías de datos para solucionar problemáticas particulares de la educación y realizar una clasificación de éstas asociadas a los diversos escenarios en los que se han aplicado.

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“…For instance, learning can be gauged based on the sequence of programming problem solving success [18], programming assignments progression [19], dialogic strategies [20], programming information seeking strategies [21], assignment submission compilation behavior [22], troubleshooting & testing behaviors [23], code snapshot process state [24], and generic Error Quotient measures [24]. Additionally, Educational Data Mining (EDM) techniques have helped educational researchers to analyze snapshots of learning processes, such as a combination of automated and semi-automated real-time coding to identify meaningful meta-cognitive planning processes in an online virtual lab environment [25]; supervised and unsupervised classification on log files and eyetracking data to find meaningful events in an exploratory learning environment [26]; the sequences of reviewing and reflecting behaviors Hidden Markov Models (HMM) to predict students' learning performances [27]. In learning analytics literature, Blikstein [28] proposed an automatic analytic tool to access student's learning in an open-ended environment.…”

Section: Behavioral Analytics In Programming Learningmentioning

confidence: 99%

Learning by Reviewing Paper-Based Programming Assessments

Paredes

Azcona

Hsiao

et al. 2018

Lifelong Technology-Enhanced Learning

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This paper presents a retrospective analysis of students' use of self-regulated learning strategies while using an educational technology that connects physical and digital learning spaces. A classroom study was carried out in a Data Structures & Algorithms course offered by the School of Computer Science. Students' reviewing behaviors were logged and the associated learning impacts were analyzed by monitoring their progress throughout the course. The study confirmed that students who had an improvement in their performance spent more time and effort reviewing formal assessments, particularly their mistakes. These students also demonstrated consistency in their reviewing behavior throughout the semester. In contrast, students who fell behind in class ineffectively reviewed their graded assessments by focusing mostly on what they already knew instead of their knowledge misconceptions.

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Discovering and Recognizing Student Interaction Patterns in Exploratory Learning Environments

Cited by 16 publications

References 9 publications

Graduate School Services: Basis for an Improvement Plan

Graduate School Services: Basis for an Improvement Plan

Data mining techniques applied in educational environments: Literature Review

Learning by Reviewing Paper-Based Programming Assessments

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