Using student data: Student-staff collaborative development of compassionate pedagogic interventions based on learning analytics and mentoring

Benkwitz, Adam; Parkes, Sarah; Bardy, Helen; Myler, Kerry; Peters, John; Akhtar, Aemal; Keeling, P. W. N.; Preece, Ryan; Smith, Tania

doi:10.1016/j.jhlste.2019.100202

Cited by 2 publications

(2 citation statements)

References 37 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Wang and Zhu ( 2019 ) observed that the use of digital technologies in teaching is capable of supporting high-quality and transformed educational process by comparing the performances/outcomes of the learners in a MOOC-based, flipped classroom, and traditional class settings, respectively. Whereas, the study by Abu Zohair ( 2019 ) that used the machine learning techniques to analyze educational datasets, shows that adequate (accurate) prediction of students’ data or analysis may have not only been crucial in improving the students' performance/experiences (Benkwitz et al, 2019 ; Crues et al, 2018 ; Kori et al, 2018 ; Weston et al, 2019 ), but also represents as a useful tool towards the promotion of the various university’s ranking or status (Medne et al, 2020 ; Mourad, 2017 ; Tóth & Surman, 2019 ).…”

Section: Background Informationmentioning

confidence: 99%

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

et al. 2021

View full text Add to dashboard Cite

Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching–learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching–learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers’ performances and recommendations based on data derived from students’ evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student’s recommendation for the teachers may be based on information about the students’ gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students’ recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed ( n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students’ comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments ( p = .000). While the males appear to be slightly borderline in terms of emotions ( p = .056) and sentiment ( p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students’ recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1...

show abstract

Section: Background Informationmentioning

confidence: 99%

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Prediction has been one of the most attractive fields of EDM since 1995 [3]. Related studies usually exploit potential factors from the university's data [4] to build a prediction model such as GPA or student's performance. Various Machine Learning algorithms are used to solve these problems including Decision Tree, Random Forest, Regression, and Neural Network [9,34,42].…”

Section: Introductionmentioning

confidence: 99%

From Transcripts to Insights for Recommending the Curriculum to University Students

Chung²,

et al. 2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

Student data play an important role in evaluating the effectiveness of educational programs in the universities. All data are aggregated to calculate the education criteria by year, region, or organization. Remarkably, recent studies showed the data impacts when making exploration to predict student performance objectives. Many methods in terms of data mining were proposed to be suitable to extract useful information in regards to data characteristics. However, the reconciliation between applied methods and data characteristics still exists some challenges. Our paper will demonstrate the analysis of this relationship for a specific dataset in practice. The paper describes a distributed framework based on Spark for extracting information from raw data. Then, we integrate machine learning techniques to train the prediction model. The experiments results are analyzed through different scenarios to show the harmony between the influencing factors and applied techniques.

show abstract

Using student data: Student-staff collaborative development of compassionate pedagogic interventions based on learning analytics and mentoring

Cited by 2 publications

References 37 publications

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

Towards teaching analytics: a contextual model for analysis of students’ evaluation of teaching through text mining and machine learning classification

From Transcripts to Insights for Recommending the Curriculum to University Students

Contact Info

Product

Resources

About