More Like this

1. Effect of Implementing Subgoals in Code.org's Intro to Programming Unit in Computer Science Principles

   https://doi.org/10.1145/3415594  

   Margulieux, Lauren E. ; Morrison, Briana B. ; Franke, Baker ; Ramilison, Harivololona ( November 2020 , ACM Transactions on Computing Education)

   null (Ed.)

   The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). Programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like Computer Science Principles and non-profit organizations like Code.org. Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in Code.org's Computer Science Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least one-third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed. 

   more » « less
2. Teaching Explicit Programming Strategies to Adolescents

   https://doi.org/10.1145/3287324.3287371  

   Ko, Amy J. ; LaToza, Thomas D. ; Hull, Stephen ; Ko, Ellen A. ; Kwok, William ; Quichocho, Jane ; Akkaraju, Harshitha ; Pandit, Rishin ( January 2019 , Symposium on Computer Science Education)

   One way to teach programming problem solving is to teach explicit, step-by-step strategies. While prior work has shown these to be effective in controlled settings, there has been little work investigating their efficacy in classrooms. We conducted a 5-week case study with 17 students aged 15-18, investigating students' sentiments toward two strategies for debugging and code reuse, students' use of scaffolding to execute these strategies, and associations between students' strategy use and their success at independently writing programs in class. We found that while students reported the strategies to be valuable, many had trouble regulating their choice of strategies, defaulting to ineffective trial and error, even when they knew systematic strategies would be more effective. Students that embraced the debugging strategy completed more features in a game development project, but this association was mediated by other factors, such as reliance on help, strategy self-efficacy, and mastery of the programming language used in the class. These results suggest that teaching of strategies may require more explicit instruction on strategy selection and self-regulation. 

   more » « less
3. From Students to Cofacilitators: Latinx Students’ Experiences in Mathematics and Computer Programming

   https://doi.org/10.1177/01614681221104104  

   LópezLeiva, Carlos A. ; Noriega, Gabino ; Celedón-Pattichis, Sylvia ; Pattichis, Marios S. ( June 2022 , Teachers College Record: The Voice of Scholarship in Education)

   Computer programming is rarely accessible to K–12 students, especially for those from culturally and linguistically diverse backgrounds. Middle school age is a transitioning time when adolescents are more likely to make long-term decisions regarding their academic choices and interests. Having access to productive and positive knowledge and experiences in computer programming can grant them opportunities to realize their abilities and potential in this field.

   This study focuses on the exploration of the kind of relationship that bilingual Latinx students developed with themselves and computer programming and mathematics (CPM) practices through their participation in a CPM after-school program, first as students and then as cofacilitators teaching CPM practices to other middle school peers.

   An after-school program, Advancing Out-of-School Learning in Mathematics and Engineering (AOLME), was held at two middle schools located in rural and urban areas in the Southwest. It was designed to support an inclusive cultural environment that nurtured students’ opportunities to learn CPM practices through the inclusion of languages (Spanish and English), tasks, and participants congruent to students in the program. Students learned how to represent, design, and program digital images and videos using a sequence of 2D arrays of hexadecimal numbers with Python on a Raspberry Pi computer. The six bilingual cofacilitators attended Levels 1 and 2 as students and were offered the opportunity to participate as cofacilitators in the next implementation of Level 1.

   This longitudinal case study focused on analyzing the experiences and shifts (if any) of students who participated as cofacilitators in AOLME. Their narratives were analyzed collectively, and our analysis describes the experiences of the cofacilitators as a single case study (with embedded units) of what it means to be a bilingual cofacilitator in AOLME. Data included individual exit interviews of the six cofacilitators and their focus groups (30–45 minutes each), an adapted 20-item CPM attitude 5-point Likert scale, and self-report from each of them. Results from attitude scales revealed cofacilitators’ greater initial and posterior connections to CPM practices. The self-reports on CPM included two number lines (0–10) for before and after AOLME for students to self-assess their liking and knowledge of CPM. The numbers were used as interview prompts to converse with students about experiences. The interview data were analyzed qualitatively and coded through a contrast-comparative process regarding students’ description of themselves, their experiences in the program, and their perception of and relationship toward CPM practices.

   Findings indicated that students had continued/increased motivation and confidence in CPM as they engaged in a journey as cofacilitators, described through two thematic categories: (a) shifting views by personally connecting to CPM, and (b) affirming CPM practices through teaching. The shift in connecting to CPM practices evolved as students argued that they found a new way of learning mathematics, in that they used mathematics as a tool to create videos and images that they programmed by using Python while making sense of the process bilingually (Spanish and English). This mathematics was viewed by students as high level, which in turned helped students gain self-confidence in CPM practices. Additionally, students affirmed their knowledge and confidence in CPM practices by teaching them to others, a process in which they had to mediate beyond the understanding of CPM practices. They came up with new ways of explaining CPM practices bilingually to their peers. In this new role, cofacilitators considered the topic and language, and promoted a communal support among the peers they worked with.

   Bilingual middle school students can not only program, but also teach bilingually and embrace new roles with nurturing support. Schools can promote new student roles, which can yield new goals and identities. There is a great need to redesign the school mathematics curriculum as a discipline that teenagers can use and connect with by creating and finding things they care about. In this way, school mathematics can support a closer “fit” with students’ identification with the world of mathematics. Cofacilitators learned more about CPM practices by teaching them, extending beyond what was given to them, and constructing new goals that were in line with a sophisticated knowledge and shifts in the practice. Assigned responsibility in a new role can strengthen students’ self-image, agency, and ways of relating to mathematics.

    

   more » « less
4. Work in Progress: An Exploration of Students’ Conceptualization of Research after Participating in an Undergraduate Research Experience

   https://doi.org/10.18260/1-2--32361  

   Tise, J. ; Zappe, S.E. ; Gomez, E.W. ; Kumar, M. ; Croninger, R.M.V. ; Cutler, S. ( January 2019 , American Society for Engineering Education)

   This Work-in-Progress paper investigates how students participating in a chemical engineering (ChE) Research Experience for Undergraduates (REU) program conceptualize and make plans for research projects. The National Science Foundation has invested substantial financial resources in REU programs, which allow undergraduate students the opportunity to work with faculty in their labs and to conduct hands-on experiments. Prior research has shown that REU programs have an impact on students’ perceptions of their research skills, often measured through the Undergraduate Research Student Self-Assessment (URSSA) survey. However, few evaluation and research studies have gone beyond perception data to include direct measures of students’ gains from program participation. This work-in-progress describes efforts to evaluate the impact of an REU on students’ conceptualization and planning of research studies using a pre-post semi-structured interview process. The construct being investigated for this study is planning, which has been espoused as a critical step in the self-regulated learning (SRL) process (Winne & Perry, 2000; Zimmerman, 2008). Students who effectively self-regulate demonstrate higher levels of achievement and comprehension (Dignath & Büttner, 2008), and (arguably) work efficiency. Planning is also a critical step in large projects, such as research (Dvir & Lechler, 2004). Those who effectively plan their projects make consistent progress and are more likely to achieve project success (Dvir, Raz, & Shenhar, 2003). Prior REU research has been important in demonstrating some positive impacts of REU programs, but it is time to dig deeper into the potential benefits to REU participation. Many REU students are included in weekly lab meetings, and thus potentially take part in the planning process for research projects. Thus, the research question explored here is: How do REU participants conceptualize and make plans for research projects? The study was conducted in the ChE REU program at a large, mid-Atlantic research-oriented university during the summer of 2018. Sixteen students in the program participated in the study, which entailed them completing a planning task followed by a semi-structured interview at the start and the end of the REU program. During each session, participants read a case statement that asked them to outline a plan in writing for a research project from beginning to end. Using semi-structured interview procedures, their written outlines were then verbally described. The verbalizations were recorded and transcribed. Two members of the research team are currently analyzing the responses using an open coding process to gain familiarity with the transcripts. The data will be recoded based on the initial open coding and in line with a self-regulatory and project-management framework. Results: Coding is underway, preliminary results will be ready by the draft submission deadline. The methods employed in this study might prove fruitful in understanding the direct impact on students’ knowledge, rather than relying on their perceptions of gains. Future research could investigate differences in students’ research plans based on prior research experience, research intensity of students’ home institutions, and how their plans may be impacted by training. 

   more » « less
5. Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events

   Buckwalter, Grace Chhin ( December 2021 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   The Temple University Hospital EEG Corpus (TUEG) [1] is the largest publicly available EEG corpus of its type and currently has over 5,000 subscribers (we currently average 35 new subscribers a week). Several valuable subsets of this corpus have been developed including the Temple University Hospital EEG Seizure Corpus (TUSZ) [2] and the Temple University Hospital EEG Artifact Corpus (TUAR) [3]. TUSZ contains manually annotated seizure events and has been widely used to develop seizure detection and prediction technology [4]. TUAR contains manually annotated artifacts and has been used to improve machine learning performance on seizure detection tasks [5]. In this poster, we will discuss recent improvements made to both corpora that are creating opportunities to improve machine learning performance. Two major concerns that were raised when v1.5.2 of TUSZ was released for the Neureka 2020 Epilepsy Challenge were: (1) the subjects contained in the training, development (validation) and blind evaluation sets were not mutually exclusive, and (2) high frequency seizures were not accurately annotated in all files. Regarding (1), there were 50 subjects in dev, 50 subjects in eval, and 592 subjects in train. There was one subject common to dev and eval, five subjects common to dev and train, and 13 subjects common between eval and train. Though this does not substantially influence performance for the current generation of technology, it could be a problem down the line as technology improves. Therefore, we have rebuilt the partitions of the data so that this overlap was removed. This required augmenting the evaluation and development data sets with new subjects that had not been previously annotated so that the size of these subsets remained approximately the same. Since these annotations were done by a new group of annotators, special care was taken to make sure the new annotators followed the same practices as the previous generations of annotators. Part of our quality control process was to have the new annotators review all previous annotations. This rigorous training coupled with a strict quality control process where annotators review a significant amount of each other’s work ensured that there is high interrater agreement between the two groups (kappa statistic greater than 0.8) [6]. In the process of reviewing this data, we also decided to split long files into a series of smaller segments to facilitate processing of the data. Some subscribers found it difficult to process long files using Python code, which tends to be very memory intensive. We also found it inefficient to manipulate these long files in our annotation tool. In this release, the maximum duration of any single file is limited to 60 mins. This increased the number of edf files in the dev set from 1012 to 1832. Regarding (2), as part of discussions of several issues raised by a few subscribers, we discovered some files only had low frequency epileptiform events annotated (defined as events that ranged in frequency from 2.5 Hz to 3 Hz), while others had events annotated that contained significant frequency content above 3 Hz. Though there were not many files that had this type of activity, it was enough of a concern to necessitate reviewing the entire corpus. An example of an epileptiform seizure event with frequency content higher than 3 Hz is shown in Figure 1. Annotating these additional events slightly increased the number of seizure events. In v1.5.2, there were 673 seizures, while in v1.5.3 there are 1239 events. One of the fertile areas for technology improvements is artifact reduction. Artifacts and slowing constitute the two major error modalities in seizure detection [3]. This was a major reason we developed TUAR. It can be used to evaluate artifact detection and suppression technology as well as multimodal background models that explicitly model artifacts. An issue with TUAR was the practicality of the annotation tags used when there are multiple simultaneous events. An example of such an event is shown in Figure 2. In this section of the file, there is an overlap of eye movement, electrode artifact, and muscle artifact events. We previously annotated such events using a convention that included annotating background along with any artifact that is present. The artifacts present would either be annotated with a single tag (e.g., MUSC) or a coupled artifact tag (e.g., MUSC+ELEC). When multiple channels have background, the tags become crowded and difficult to identify. This is one reason we now support a hierarchical annotation format using XML – annotations can be arbitrarily complex and support overlaps in time. Our annotators also reviewed specific eye movement artifacts (e.g., eye flutter, eyeblinks). Eye movements are often mistaken as seizures due to their similar morphology [7][8]. We have improved our understanding of ocular events and it has allowed us to annotate artifacts in the corpus more carefully. In this poster, we will present statistics on the newest releases of these corpora and discuss the impact these improvements have had on machine learning research. We will compare TUSZ v1.5.3 and TUAR v2.0.0 with previous versions of these corpora. We will release v1.5.3 of TUSZ and v2.0.0 of TUAR in Fall 2021 prior to the symposium. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation’s Industrial Innovation and Partnerships (IIP) Research Experience for Undergraduates award number 1827565. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] I. Obeid and J. Picone, “The Temple University Hospital EEG Data Corpus,” in Augmentation of Brain Function: Facts, Fiction and Controversy. Volume I: Brain-Machine Interfaces, 1st ed., vol. 10, M. A. Lebedev, Ed. Lausanne, Switzerland: Frontiers Media S.A., 2016, pp. 394 398. https://doi.org/10.3389/fnins.2016.00196. [2] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Frontiers in Neuroinformatics, vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [3] A. Hamid et, al., “The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts.” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1-3. https://ieeexplore.ieee.org/document/9353647. [4] Y. Roy, R. Iskander, and J. Picone, “The NeurekaTM 2020 Epilepsy Challenge,” NeuroTechX, 2020. [Online]. Available: https://neureka-challenge.com/. [Accessed: 01-Dec-2021]. [5] S. Rahman, A. Hamid, D. Ochal, I. Obeid, and J. Picone, “Improving the Quality of the TUSZ Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1–5. https://ieeexplore.ieee.org/document/9353635. [6] V. Shah, E. von Weltin, T. Ahsan, I. Obeid, and J. Picone, “On the Use of Non-Experts for Generation of High-Quality Annotations of Seizure Events,” Available: https://www.isip.picone press.com/publications/unpublished/journals/2019/elsevier_cn/ira. [Accessed: 01-Dec-2021]. [7] D. Ochal, S. Rahman, S. Ferrell, T. Elseify, I. Obeid, and J. Picone, “The Temple University Hospital EEG Corpus: Annotation Guidelines,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/tuh_eeg/annotations/. [8] D. Strayhorn, “The Atlas of Adult Electroencephalography,” EEG Atlas Online, 2014. [Online]. Availabl 

   more » « less