Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey

ChatGPT has sparked both excitement and skepticism in education. To analyze its impact on teaching and learning it is crucial to understand how students perceive ChatGPT and assess its potential and challenges. Toward this, we conducted a two-stage study with senior students in a computer engineering program ( n=56 ). In the first stage, we asked the students to evaluate ChatGPT using their own words after they used it to complete one learning activity. The returned responses (3136 words) were analyzed by coding and theme building (36 codes and 15 themes). In the second stage, we used the derived codes and themes to create a 27-item questionnaire. The students responded to this questionnaire three weeks later after completing other activities with the help of ChatGPT. The results show that the students admire the capabilities of ChatGPT and find it interesting, motivating, and helpful for study and work. They find it easy to use and appreciate its human-like interface that provides well-structured responses and good explanations. However, many students feel that ChatGPT’s answers are not always accurate and most of them believe that it requires good background knowledge to work with since it does not replace human intelligence. So, most students think that ChatGPT needs to be improved but are optimistic that this will happen soon. When it comes to the negative impact of ChatGPT on learning, academic integrity, jobs, and life, the students are divided. We conclude that ChatGPT can and should be used for learning. However, students should be aware of its limitations. Educators should try using ChatGPT and guide students on effective prompting techniques and how to assess generated responses. The developers should improve their models to enhance the accuracy of given answers. The study provides insights into the capabilities and limitations of ChatGPT in education and informs future research and development.

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Published in the IEEE Education Society Section

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

Future spectrum sharing rules very likely will be based on device environment: indoors or outdoors. For example, the 6 GHz rules created different power regimes for unlicensed devices to protect incumbents: “indoor” devices, subject to lower transmit powers but not required to access an Automatic Frequency Control database to obtain permission to use a channel, and “outdoor” devices, allowed to transmit at higher power but required to do so to determine channel availability. However, since there are no reliable means of determining if a wireless device is indoors or outdoors, other restrictions were mandated: reduced power for client devices and indoor access points that cannot be battery powered, have detachable antennas or be weatherized. These constraints lead to sub-optimal spectrum usage and potential for misuse. Hence, there is a need for robust identification of device environments to enable spectrum sharing. In this paper we study automatic indoor/outdoor classification based on the radio frequency (RF) environment experienced by a device. Using a custom Android app, we first create a labeled data set of a number of parameters of Wi-Fi and cellular signals in various indoor and outdoor environments, and then evaluate the classification performance of various machine learning (ML) models on this data set. We find that tree-based ensemble ML models can achieve greater than 99% test accuracy and F1-Score, thus allowing devices to self-identify their environment and adapt their transmit power accordingly.

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