ABSTRACT
The purpose of this research is to verify the method in the elicitation of latent needs from consumer needs by conducting the working prototype-based interview and collecting raw data which is responses from the consumer. Latent needs are those that many consumers recognize as important in a final product but do not or cannot articulate in advance. The challenge in identifying latent needs is finding the method to elicit from consumers the needs which are not addressed by any inventors yet in the present market but would delight consumers if delivered tomorrow. After conducting a consumer feedback questionnaire via the internet, a working prototype was created. The working prototype then was used as material to prepare presentation slides. The first presentation slides were focused on the background problems and ideas for the solutions while the second presentation slides provided consumers with a prototype and story of the product that was believed would be one of the solutions to the problems. Based on the results, latent needs interpreted from interviewees’ responses and the categories of the needs obtained from the Prototype-based interviews are more than from the Problem-based interview. The latent needs that we were able to obtain from this research were for example, “The device is able to detect small changes in a child while changing a diaper” and “The device is able to detect small changes in a child while watching he/she sleeping” which could lead into the prevention of unwanted incident such as sudden infant death syndrome (SIDS). This supports our assumption that showing working prototype based materials with story descriptions can be effective in uncovering potential latent needs. However, due to the COVID-19 pandemic, we were unable to give the interviewees chances to touch and look closely at the working prototype therefore latent needs possibly gained from this experience are still uncovered. Although there are still limitations in our findings, the method that we proposed is able to support discovering latent needs in future. © 2022 The Japan Society of Mechanical.
ABSTRACT
In last two years, universities around the world have been using hyflex teaching due to COVID-19. This allows students to attend physical/online lectures in a flexible manner. A hyflex class comprises classroom students as well as online students. In this paper, we present a model for hyflex classrooms that highlights 4Cs: Content, Collaboration, Community and Communication. Based on the 4C model, a hyflex classroom has been designed and implemented through various teaching/learning tools or elements. These include the effective use of presentation slides, annotations, chatbox, open education resources, multiple choice exercises, group exercises etc. The effectiveness of these tools/elements were evaluated by means of an initial student survey. These results provide valuable insights into hyflex teaching/learning. © 2022 IEEE.
ABSTRACT
After an eight year hiatus, I was asked suddenly to teach eight sections, each consisting of twelve students, of the Materials Science Lab course (306) in fall 2020 following the COVID-19 pandemic. During the past eight years, my instructional materials for 306 had been adopted by other instructors who left online quizzes and online surveys unchanged, and made modest changes to my presentation slides, and lab report templates and formats. In fall 2020, faculty at my university chose their own modality of instruction - in-person, online or a hybrid mode. I chose asynchronous instead of synchronous because of issues related to the large number of lab sections, and the unpredictability of the impacts of the COVID-19 pandemic. In spring 2021, I am teaching ten sections of this lab course. I have made significant improvements to my instructional materials from the fall semester that are included in this paper. I wanted to give my students a lab experience that would be equal to or better than the traditional in-person experience prior to the pandemic. In most traditional in-person experiments, students measure physical dimensions such as thickness, width and diameter at various length scales using instruments such as an ordinary ruler, a Vernier caliper, or a micrometer. To implement similar measurements online, I decided to integrate image analysis using FijI based on ImageJ software, and use it as a video caliper tool to measure features in ordinary and high resolution images. Traditionally, 306 and courses similar to it at other universities focus almost exclusively on experimental techniques to measure properties and characterize materials. In restructuring the course, I decided to add comparable emphasis on structure and processing of materials as it relates to testing and characterization of metallic materials. This paper describes the first three of the seven experiments that were developed focusing on: (1) Introductory Image Analysis and Brinell hardness testing, (2) Strengthening mechanisms and tensile testing, and (3) Cold working and Rockwell hardness testing. In each experiment, students are instructed in theory, principles, and methods using a YouTube video of narrated slides and board work, and laboratory demonstration. They are given images and data from the experiment, and data and report templates. Students make their own measurements using FijI, perform data analysis using Excel, and submit a concise lab report with critical evaluation of results and summary conclusions. The paper includes the course schedule listing the topics for each lab, grading policy, objectives for the first lab, and supporting instructional elements including the lab report grading rubric, and sample quiz questions. It also includes the survey collected after each lab, and response statistics from the first lab. Actions taken in response to the student feedback are also included. © American Society for Engineering Education, 2021