Student Teacher Interaction While Learning Computer Science: Early
Results from an Experiment on Undergraduates
- URL: http://arxiv.org/abs/2307.03802v1
- Date: Fri, 7 Jul 2023 19:08:59 GMT
- Title: Student Teacher Interaction While Learning Computer Science: Early
Results from an Experiment on Undergraduates
- Authors: Manuela Petrescu, Kuderna Bentasup
- Abstract summary: The scope of this paper was to find out how the students in Computer Science perceive different teaching styles.
Students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The scope of this paper was to find out how the students in Computer Science
perceive different teaching styles and how the teaching style impacts the
learning desire and interest in the course. To find out, we designed and
implemented an experiment in which the same groups of students (86 students)
were exposed to different teaching styles (presented by the same teacher at a
difference of two weeks between lectures). We tried to minimize external
factors' impact by carefully selecting the dates (close ones), having the
courses in the same classroom and on the same day of the week, at the same
hour, and checking the number and the complexity of the introduced items to be
comparable. We asked for students' feedback and we define a set of countable
body signs for their involvement in the course. The results were comparable by
both metrics (body language) and text analysis results, students prefer a more
interactive course, with a relaxing atmosphere, and are keener to learn in
these conditions.
Related papers
- Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
We integrate insights from machine teaching and pragmatic communication with the literature on representational alignment.
We design a supervised learning environment that disentangles representational alignment from teacher accuracy.
arXiv Detail & Related papers (2024-06-06T17:48:24Z) - A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs [0.0]
We analyze a heterogeneous knowledge graph consisting of students, course videos, formative assessments and their interactions to predict student performance.
We then compare the models generated between 5 on-campus and 2 fully-online MOOC-style instances of the same course.
The model developed achieved a 70-90% accuracy of predicting whether a student would pass a particular problem set based on content consumed, course instance, and modality.
arXiv Detail & Related papers (2024-05-19T03:33:59Z) - Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
We introduce a suite of models and evaluation methods we call AdapT.
AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs.
Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
arXiv Detail & Related papers (2024-05-07T17:05:27Z) - Enhancing Students' Learning Process Through Self-Generated Tests [0.0]
This paper describes an educational experiment aimed at the promotion of students' autonomous learning.
The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams.
Questions uploaded by students are visible to every enrolled student as well as to each involved teacher.
arXiv Detail & Related papers (2024-03-21T09:49:33Z) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z) - Generalized Knowledge Distillation via Relationship Matching [53.69235109551099]
Knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks.
Knowledge distillation extracts knowledge from the teacher and integrates it with the target model.
Instead of enforcing the teacher to work on the same task as the student, we borrow the knowledge from a teacher trained from a general label space.
arXiv Detail & Related papers (2022-05-04T06:49:47Z) - Iterative Teacher-Aware Learning [136.05341445369265]
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency.
We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
arXiv Detail & Related papers (2021-10-01T00:27:47Z) - A literature survey on student feedback assessment tools and their usage
in sentiment analysis [0.0]
We evaluate the effectiveness of various in-class feedback assessment methods such as Kahoot!, Mentimeter, Padlet, and polling.
We propose a sentiment analysis model for extracting the explicit suggestions from the students' qualitative feedback comments.
arXiv Detail & Related papers (2021-09-09T06:56:30Z) - Correlations Between Learning Environments and Dropout Intention [0.0]
This research is comparing learning environments to students dropout intentions.
While using statistics I looked at data and the correlations between two articles to see how the two studies looked side to side.
arXiv Detail & Related papers (2021-05-07T10:08:47Z) - How Does a Student-Centered Course on Communication and Professional
Skills Impact Students in the Long Run? [0.0]
This paper presents a long-term study about the effects of a student-centered course on students' thoughts, attitudes, and behavior.
The course is offered at a European university as part of a computer science master's program.
Our findings suggest that the course provided significant learning for the vast majority of respondents.
arXiv Detail & Related papers (2021-01-04T10:51:40Z) - Social Interactions Clustering MOOC Students: An Exploratory Study [57.822523354358665]
Comments were categorized based on how students interacted with them, e.g., how a student's comment received replies from peers.
Statistical modelling and machine learning were used to analyze comment categorization, resulting in 3 strong and stable clusters.
arXiv Detail & Related papers (2020-08-10T09:32:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.