Descriptive Models Of Decision Making

Abstract

How should people decide in situations involving risk, time, uncertainty, and multiattribute alternatives? How do people actually decide? In this article, we contrast the rational decision maker with the modal subject observed in experiments and surveys. We use a simple setup to present preference models and decision processes that are descriptively more accurate than the rational model.

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

“…This was done to show the technical and scholastic ways on how to utilize pieces of evidence idiosyncratically drawn from educational dataset to inform and improve the teaching quality and performances for the stakeholders (teachers and students). The model (EPDM + ML) was developed through the amalgamation of the Text mining and Machine learning technique we grounded on the descriptive decision theory (Baucells & Katsikopoulos, 2011 ; Chandler, 2017 ) which studies the rationale behind the decisions that users (e.g., students) are disposed to make by means of the textual data quantification and statistical analysis. Studies that have looked into the text mining (e.g., sentiment analysis) method and its main application within the different studied contexts, have shown that machine learning techniques can be a good predictor of the students’ feedback and/or recommendation of the teachers’ performances or outcomes (Abu Zohair, 2019 ; Altrabsheh et al, 2014 ; De Fortuny et al, 2013 ; Dey et al, 2016 ; Litman & Forbes-Riley, 2004 ; Ofli et al, 2016 ).…”

“…The outcome of their approach was a teaching outcome model (TOM) that showed to be useful not only for understanding the state-of-the-art methods or educational technologies related to teaching analytics and its implications for the future of education, prospects or mechanism in higher educational settings, but also in understanding the connection between the conceptual frameworks of teaching analytics (TA) (Ndukwe & Daniel, 2020 ), Learning Analytics (LA) (Ferguson, 2012 ; Herodotou, et al, 2019a , b ; Jones, 2019 ; Papamitsiou & Economides, 2014 ; Renz & Hilbig, 2020 ; Romero & Ventura, 2020 ), and learning design (LD) (Holmes et al, 2019 ; Mangaroska & Giannakos, 2019 ) in general. Accordingly, this study proposes the Educational Process and Data Mining plus Machine Learning model (EPDM + ML) that is built on conceptual frameworks of the TA, LA, and LD that is based on descriptive decision theory (Baucells & Katsikopoulos, 2011 ; Chandler, 2017 ) to provide a data-focused or analytical method that shows to be useful not only towards understanding of the teachers-students learning processes/outcomes to help inform and improve the quality of teaching pedagogies, but also the need for creating teaching-data-literacy or contextual-based analysis to uncover and address the different teaching–learning challenges that can be found within the higher education settings. The method (EPDM + ML) is described in detail in the next section of this paper.…”

“…Whereas, with Machine learning methods, we note that the technique can be used to predict (e.g., through automatic classification) (De Fortuny et al, 2013 ; Ofli et al, 2016 ) the relationships that exist between the process instances in connection to the processes in question. Indeed, the congruence of the aforementioned features of both methods (Text mining and Machine learning) can be harnessed to understand and foster the teaching–learning processes for the said stakeholders (HEIs, teachers, and students) based on the descriptive decision theory or concept (Baucells & Katsikopoulos, 2011 ; Chandler, 2017 ) as demonstrated in this paper. Typically, whilst the sentiments/emotions or experiences of the users (e.g., learners) can be determined from data collected about the teachers-students interactions through the text mining technique, the machine learning technique or classification models, on the other hand, can be used to predict what the learners' recommendation or assessment about the teachers-students interactions could be based on the expressed/extracted sentiments or emotional valence (Abu Zohair, 2019 ; Bollen et al, 2011 ; Dey et al, 2016 ; Ghosh et al, 2020 ).…”

“…Along these lines, this study shows that there is a need for innovative methods or approaches, such as the EPDM + ML model proposed in this paper, for extraction of educational-based information from the unprecedented datasets recorded and stored about the students' evaluation/recommendation of the teaching–learning performances, to help transliterate them into actionable plans for education in general. In our analysis, we extended the educational process and data mining (EPDM) model proposed in Okoye et al, ( 2020 ) to show how the amalgamation of the Text mining and Machine learning techniques which we grounded on the descriptive decision theory (Baucells & Katsikopoulos, 2011 ; Chandler, 2017 ), can be used to analyze the (educational) data (SET) towards improvement of the end-to-end teachers-students learning process and interactions within the higher educational setting. To this end, we proposed an educational process and data mining + machine learning (EPDM + ML) model for fostering the teaching analytics and performance evaluation.…”

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

“…To this end, the study applied a two-step (mixed) methodology grounded on synthesis of the Data-structure approach [49] and the Descriptive decision theory [50], [51] to study the rationale behind the decisions that the learners are disposed to make by means of the textual data quantification (qualitative approach) and statistical analysis (quantitative approach). This was done using the students' evaluation of teaching (SET) dataset collected in a higher education setting.…”