-
Notifications
You must be signed in to change notification settings - Fork 0
/
research.html
executable file
·151 lines (135 loc) · 8.18 KB
/
research.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Supporting students in accurately evaluating their programming abilities</title>
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"
integrity="sha384-QWTKZyjpPEjISv5WaRU9OFeRpok6YctnYmDr5pNlyT2bRjXh0JMhjY6hW+ALEwIH" crossorigin="anonymous">
<style>
h2 {
margin-top: 30px;
margin-bottom: 20px;
}
</style>
</head>
<body>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"
integrity="sha384-YvpcrYf0tY3lHB60NNkmXc5s9fDVZLESaAA55NDzOxhy9GkcIdslK1eN7N6jIeHz"
crossorigin="anonymous"></script>
<nav class="navbar navbar-expand-lg static-top navbar-light bg-light">
<div class="container-fluid">
<button class="navbar-toggler" type="button" data-bs-toggle="collapse"
data-bs-target="#navbarSupportedContent" aria-controls="navbarSupportedContent" aria-expanded="false"
aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav me-auto mb-2 mb-lg-0">
<li class="nav-item">
<a class="nav-link" href="index.html">Home</a>
</li>
<li class="nav-item">
<a class="nav-link" href="details.html">Workshop details</a>
</li>
</li>
<li class="nav-item">
<a class="nav-link active" href="research.html">Research</a>
</li>
</ul>
</div>
</div>
</nav>
<div class="container">
<h2>Background Research</h2>
<p>
This workshop is grounded in our previous research. On this page, we present a brief summary and provide
references for additional reading.
</p>
<ul>
<li>Previous research [5] has shown that students often self-assess their programming abilities negatively
in
moments that are typical in the programming process</li>
<ul>
<li>For example, a student may believe they are doing poorly when they need to stop to plan on their
assignment, even though their professors would expect them to do that as a part of the programming
process.
</li>
<li>Women also tend to be more self-critical of their programming abilities in these moments, and
students
of all genders tend to be more self-critical when comparing themselves to women [6]
</li>
</ul>
<li>Our recent work [1] has explored students' reasons for their self-assessments (e.g., they think that
they
should know enough to not need to plan), as well as *sources* of information for their self-assessment
criteria (e.g., they see their peers work without planning)
</li>
<ul>
<li>We found that students cited their professors as powerful sources for self-assessment criteria, but
these sources are not available for all self-assessment moments.
</li>In lieu of information about what is expected of them as learners, students make assumptions from
incomplete information about what is expected of them.
<li><b>This motivates our desire to co-design with faculty interventions that leverage students’ trust
in
their professor to provide sources of self-assessment criteria to students in a scalable manner.
</b></li>
</ul>
</ul>
<h2>References</h2>
<p>[1] Melissa Chen, Yinmiao Li, and Eleanor O’Rourke. 2024. Understanding the Reasoning Behind Students’
Self-Assessments of Ability in Introductory Computer Science Courses. In <i>ACM Conference on International
Computing Education Research V.1 (ICER ’24 Vol. 1)</i>, August 13–15, 2024, Melbourne, VIC, Australia.
ACM, New
York, NY,
USA, 13
pages. <a href="https://melissaychen.com/assets/files/ICER_2024_Reasons_for_Self_Assessment.pdf"
target="_blank">[PDF]</a></p>
<p>[2] Jamie Gorson, Kathryn Cunningham, Marcelo Worsley, and Eleanor O’Rourke. 2022. Using Electrodermal
Activity Measurements to Understand Student Emotions While Programming. In <i>Proceedings of the 2022 ACM
Conference on International Computing Education Research V.1, August 03, 2022. ACM, Lugano and Virtual
Event
Switzerland</i>, 105–119. <a href="https://doi.org/10.1145/3501385.3543981" target="_blank">[LINK]</a>
</p>
<p>[3] Jamie Gorson, Nicholas LaGrassa, Cindy Hsinyu Hu, Elise Lee, Ava Marie Robinson, and Eleanor O’Rourke.
2021. An Approach for Detecting Student Perceptions of the Programming Experience from Interaction Log Data.
In <i>Artificial Intelligence in Education</i>, Ido Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin
and
Vania Dimitrova (eds.). Springer International Publishing, Cham, 150–164.
<a href="https://doi.org/10.1007/978-3-030-78292-4_13" target="_blank">[LINK]</a>
</p>
<p>[4] Jamie Gorson and Eleanor O’Rourke. 2019. How Do Students Talk About Intelligence?: An Investigation of
Motivation, Self-efficacy, and Mindsets in Computer Science. In <i>Proceedings of the 2019 ACM Conference on
International Computing Education Research, July 30, 2019. ACM, Toronto ON Canada</i>, 21–29.
<a href="https://doi.org/10.1145/3291279.3339413" target="_blank">[LINK]</a>
</p>
<p>[5] Jamie Gorson and Eleanor O’Rourke. 2020. Why do CS1 Students Think They’re Bad at Programming?
Investigating Self-efficacy and Self-assessments at Three Universities. In <i>Proceedings of the 2020 ACM
Conference on International Computing Education Research (ICER ’20)</i>, August 07, 2020. Association
for
Computing Machinery, New York, NY, USA, 170–181. <a href="https://doi.org/10.1145/3372782.3406273"
target="_blank">[LINK]</a>
</p>
<p>[6] Jamie Gorson and Eleanor O’Rourke. 2021. CS1 Student Assessments of Themselves Relative to Others: The
Role of Self-Critical Bias and Gender. <i>ISLS (2021)</i>, 597–600. <a
href="https://repository.isls.org//handle/1/7534" target="_blank">[LINK]</a>
</p>
<p>[7] Yinmiao Li, Melissa Chen, Ayse Hunt, Haoqi Zhang, and Eleanor O’Rourke.
2024. Exploring the Interplay of Metacognition, Affect, and Behaviors in an
Introductory Computer Science Course for Non-Majors. In <i>ACM Conference
on International Computing Education Research V.1 (ICER ’24 Vol. 1), August
13–15, 2024, Melbourne, VIC, Australia</i>. ACM, New York, NY, USA, 15 pages.
<a href="assets/Li_et_al_2024_MetacognitionAffectBehaviors.pdf" target="_blank">[PDF]</a>
</p>
</div>
<div class="container">
<footer class="py-3 my-4">
<ul class="nav justify-content-center border-top pb-3 mb-3">
<li class="nav-item"><a href="index.html" class="nav-link px-2 text-body-secondary">Home</a></li>
<li class="nav-item"><a href="details.html" class="nav-link px-2 text-body-secondary">Details</a></li>
<li class="nav-item"><a href="research.html" class="nav-link px-2 text-body-secondary">Research</a></li>
</ul>
</footer>
</div>
</body>
</html>