=Paper=
{{Paper
|id=Vol-3927/paper14
|storemode=property
|title=A Synthesis Proposal: Merging AI in Education with Automata Theory
|pdfUrl=https://ceur-ws.org/Vol-3927/paper14.pdf
|volume=Vol-3927
|authors=Andre Kenneth Chase Randall
|dblpUrl=https://dblp.org/rec/conf/ectel/Randall24
}}
==A Synthesis Proposal: Merging AI in Education with Automata Theory==
https://ceur-ws.org/Vol-3927/paper14.pdf
A Synthesis Proposal: Merging AI in Education with
Automata Theory
Andre Kenneth Chase Randall
University of Massachusetts Amherst (UMass Amherst), Amherst, MA 01003 USA
Abstract
As classroom sizes expand, formal education faces increased challenges in providing scalable, targeted feedback based on
student engagement. đśđ250, an undergraduate core course in discrete mathematics, covers topics such as logic, elementary
number theory, proof by induction, recursion on trees, search algorithms, regular languages, finite state machines, and
computability. These concepts often present challenges due to their abstract nature and the precision required in logical
reasoning. Primarily enrolling computer science and related majors, the course benefits from reusable learning objects
(RLOs) designed to support concept mastery. In this context, the author proposed a new discussion material on mathematical
foundations, specifically targeting regular language expression. He tested a Python tool that allowed students to check their
answersâ correctness while mastering regular language expressions. Students completed the Python tool and a survey, which
confirmed the toolâs usefulness and provided valuable feedback for iterative design.
This paper aims to contribute to the existing body of knowledge on AI in education by shedding light on student
perspectives with RLOs. In future iterations, we plan to recruit a more diverse range of educators, including female educators
from all-women colleges, to broaden our perspective on instructional effectiveness. Moving forward, we seek to explore the
balance between technological and human interventions required for effective course delivery. Although these findings are
preliminary, continued research and richer data may reveal organic, inductive themes as this iterative process unfolds.
Keywords
AI in Education, Automata Theory, Synthesis Proposal, Doctoral Consortium, Regular Expressions, Reusable Learning Objects
(RLOs)
1. Introduction The RLO includes several key components: a Python-
based interactive tool for answer verification, structured
As classroom sizes expand, educators increasingly need practice exercises, and integrated feedback mechanisms
scalable, targeted feedback mechanisms to support stu- that guide students through the learning process. De-
dent engagement. đśđ250, an undergraduate core course signed to actively support students, the RLO delivers
in discrete mathematics at UMass Amherst, covers key immediate feedback to help them correct errors and
topics such as logic, elementary number theory, proof strengthen their understanding. During the study, the
by induction, recursion, search algorithms, regular lan- author assigned a treatment group to use the RLO for
guages, finite state machines, and aspects of computabil- regular expression exercises, where students received
ity [1]. These concepts challenge students due to their instant feedback as they practiced. The study also in-
abstract nature and the logical precision they demand. cluded pre- and post-surveys with the treatment group,
To address these challenges, the author developed a capturing changes in understanding and perceptions.
Reusable Learning Object (RLO), which refers to âany Survey responses indicated that the RLO was primarily
digital resource that can be reused to support learningâ used for reinforcing course material and problem-solving
[2]. The RLO incorporates modular, adaptable resources skills, with many participants planning to apply these
that support specific educational goals. In this context, skills directly to their đśđ250 assignments and exams.
the RLO focuses on simplifying complex topics in đśđ250 Participant #2 noted, âYes, it was very useful in helping
through interactive and reusable digital content, helping check work,â while Participant #5 highlighted the toolâs
students engage independently with difficult concepts, benefit in understanding regular expressions, stating, âI
especially those involving regular expressions. would recommend it to others because it was helpful in
understanding how regex are set up.â However, some
EC-TEL 2024: Doctoral Consortium for Nineteenth European Confer- participants felt the toolâs feedback was not sufficiently
ence on Technology Enhanced Learning, September 16 - 20, 2024, Krems,
personalized, as Participant #6 remarked, âIt wasnât per-
Austria
*
Per application process, Doctoral Consortium paper authored by sonalized, but it was useful,â indicating that while benefi-
student himself with acknowledgments of supervisor and collabo- cial, it did not offer individualized guidance.
rators at the end of the paper. A number of participants also suggested improvements
$ technicalchase@gmail.com (A. K. C. Randall) to make the tool more user-friendly and accessible. For
 https://technicalchase.com/ (A. K. C. Randall)
instance, Participant #3 recommended âmore language
0000-0002-3971-3775 (A. K. C. Randall)
Š 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License optionsâ and âtext-to-speechâ functionality, reflecting a
Attribution 4.0 International (CC BY 4.0).
1
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
desire for inclusive design to meet diverse learning needs. development and user feedback, ensuring that adaptive
Initial usability challenges were also reported, with Par- tools like ITS are aligned with real-world educational
ticipant #1 mentioning, âI wasnât sure how to use the tool needs and specific challenges faced by students, instruc-
at first,â emphasizing the importance of an intuitive user tors, and administrators [11]. This process aligns with
experience. Participant #15 expressed reluctance to rec- AIâs objectives in education by fostering adaptable and
ommend the tool, commenting, âI would not recommend scalable solutions suitable for diverse learning contexts.
the tool to others; it did not explain very much and took Implementing a SWOT (Strengths, Weaknesses, Op-
a long time to run,â highlighting areas for improvement portunities, Threats) analysis further aids in evaluating
in efficiency and instructional depth. While the RLO was the feasibility of adaptive learning technologies, identi-
seen as helpful for coursework, some participants were fying strengths like personalized learning pathways and
uncertain about its long-term applicability, as Participant addressing limitations such as data privacy concerns [?
#12 noted, âI do not plan to use it in my personal life,â ]. Together, Steve Blankâs framework and SWOT anal-
suggesting that its perceived value was largely limited ysis underscore the importance of developing adaptive
to immediate academic goals. educational tools that effectively respond to the evolving
Through an iterative design process, the author struc- needs of educational stakeholders [8].
tured the RLO to enhance student engagement by al- Moreover, adaptive algorithms similar to those em-
lowing them to interact with content at their own pace. ployed in fields like gamingâwhere neural networks,
In this paper, the author uses the terms "Learning Ob- enhanced by genetic algorithms, refine responses based
ject" and "Reusable Learning Object" interchangeably to on real-time performance feedbackâillustrate how it-
describe these modular educational resources [3]. erative adaptation could similarly benefit educational
Beyond promoting independent learning, the RLO contexts [12]. Such an approach could allow educational
aims to increase engagement, strengthen problem- AI systems to adjust dynamically to varying learner pro-
solving skills, and improve studentsâ ability to master gressions, supporting engagement and promoting per-
abstract mathematical concepts. Prior research highlights sonalized learning paths across diverse skill levels.
how learning analytics and feedback within RLOs can
enhance learning outcomes, especially in skill-based sub-
jects like those in đśđ250 [4, 5] 3. Study Design
The next sections address the related works, the study
The study design happened as an iterative process. First
design, development, and testing to ensure the RLOâs
the authored created a pilot project as a proof of concept.
reusability. Finally, the author outlined a plan for a proof
Thereafter, the author became the entrepreneurial lead
of concept, drawing from the doctoral program insights,
for Team Intelligent Tutoring Systems R Us. Team In-
related works and testing within real-world educational
telligent Tutoring Systems R Us obtained U. S. National
contexts. Future endeavors, especially in the context of
Science Foundationâs Innovation Corps (I - Corpsâ˘) Cus-
a doctoral program, require continuous refinement and
tomer Discovery Project funding.
optimization of the initial concept as presented herein to
achieve the best outcomes.
3.1. Course Pilot Project
2. Related Works As part of teaching assistant preparation course, the au-
thor and classmates conducted a pilot study without In-
As classroom sizes expand, educators face increased chal- stitution Review Board (IRB) approval. The pilot study
lenges in providing scalable, targeted feedback based on ended with 147 participants in March-May 2022.
student engagement [6]. The application of AI in educa- In pilot study, they introduced a python script and
tion addresses these issues through tools like Intelligent sought to probe studentsâ reactions to its usefulness as a
Tutoring Systems (ITS), adaptive content creation, and au- RLO.
tomated administrative tasks [7, 8]. AI in education com- They designed the RLO to enhance the studentsâ un-
bines multiple fieldsâlearning science, human-computer derstanding of a regular expressions. During the pilot
interaction (HCI), software engineering, natural language study, they gave participants the Python script along with
processing (NLP), and machine learning (ML) [9]. How- instructional materials for completing a series of tasks.
ever, developing effective ITS tools requires seamless The study concluded with a survey featuring open-ended
integration of advanced algorithms, deep pedagogical questions to collect qualitative data on the studentsâ ex-
knowledge, and user-centered design [10]. periences and feedback.
Steve Blankâs Customer Discovery framework, as out- The survey responses varied significantly. Over 31.3%
lined in the NSF I-Corps Teaching Handbook, reinforces of participants reported âno challenges,â indicating a
this interdisciplinary approach by emphasizing iterative smooth experience with the Python script. In contrast,
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
under 15.6% of participants expressed difficulties, with 4. Administrators aim to boost institutional rank-
comments such as âI found the instructions hard to follow ing through future-generation technologies while
and get the code running.â These responses highlighted addressing academic integrity, accessibility, and
areas for instructional materials improvements. privacy concerns.
Additionally, 24.7% of participants, particularly those
majoring in computer science, showed motivation to- 3.2.2. I-Corps Study Methods
wards the concept of using programming in their course-
work. These students appreciated the practical appli- 1. Customer Segmentation: The team focused on
cation of programming skills and expressed interest in understanding the mental models of STEM stu-
further integrating such tools into their studies. dents, professors, and administrators, exploring
Overall, the pilot study provided valuable insights into their behaviors, characteristics, and needs.
the effectiveness of the RLO and the Python script. The 2. Customer Discovery Interviews: Using a hy-
feedback collected not only show improved student learn- brid approach of virtual and in-person interviews,
ing experience but also aided the instructional materials the team engaged participants to uncover educa-
refinement as part of an IRB protocol (See Figure#1). tional pain points and evaluate the RLOâs com-
mercial viability.
3. Data Collection and Analysis: With over 90
3.2. U.S. National Science Foundationâs
contacts from 15 colleges and universities, the
Innovation Corps (I-Corpsâ˘) team conducted 35 detailed interviews with stu-
Customer Discovery Project dents, instructors, and administrative profession-
The National Science Foundation (NSF) I-Corps Teams als in instructional roles.
program provides an intensive seven-week entrepreneur-
ship training course with mentorship and funding for 3.2.3. I-Corps Conclusion
customer discovery. As part of this program, the author The team initially accepted the null hypothesis but discov-
led Team Intelligent Tutoring Systems R Us as the en- ered a strategic pivot by broadening customer segmenta-
trepreneurial lead, receiving a travel grant supported by tion beyond the host institution. This pivot enabled the
Cornell Tech and the National GEM Consortium to ex- development of a more dynamic business model, contin-
plore market potential for the Reusable Learning Object uously revised through customer insights. Aligning with
(RLO) project [13]. Woolf et al.âs AI Grand Challenges, future goals include
The I-Corps program guided the team in conducting creating intelligent tutoring systems, real-world simu-
a hybrid field study to test initial hypotheses and make lation environments, and natural language processing
adaptive pivots. Insights from customer discovery in- capabilities to enhance adaptive learning [8, 7].
terviews were essential in refining the RLOâs interface
and feedback mechanisms. This process identified key
challenges, value propositions, and market opportunities, 4. Study Development
structured within a business model framework as illus-
trated in the SWOT analysis (See Figure 2). Future work This study employed a mixed-methods research design,
includes developing a comprehensive Strengths, Weak- consisting of both quantitative and qualitative data col-
nesses, Opportunities, and Threats (SWOT) analysis as lection and analysis. The author conducted three phases:
preparation for a national I-Corps application and further
expanding the studyâs scope. ⢠Phase 1: Literature Review and Case Studies. The
first phase of the study involved another compre-
hensive review of the literature on the use of AI
3.2.1. I-Corps Research Questions and Hypotheses
in Education. This phase helped to identify best
1. What value propositions exist for students, profes- practices and potential challenges associated with
sors, and administrators in using adaptive learn- using AI in Education. During this phase, the au-
ing tools for instructional support? thor worked with an undergraduate student to
2. STEM students seek adaptive tools to aid in con- complete an Honors Thesis Project, where the
tent mastery and skill development, including author served as a committee member.
grit, motivation, and soft skills beyond technical ⢠Phase 2: Surveys and Interviews. The second
competence. phase of the study involved the administration
3. STEM instructors desire adaptive tools that sim- of surveys and interviews to educators, teaching
plify grading, enable course scalability, and intro- assistants, and students. The surveys and inter-
duce innovative learning experiences. views assess perceptions of RLOs and its potential
impact and other considerations.
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
Figure 1: Team Intelligent Tutoring Systems R Us utilized Steve Blankâs Customer Discovery framework and Alexander
Osterwalderâs Business Model Generation along with methods used by the National Science Foundation (NSF).
⢠Phase 3: Analysis and Synthesis. The third phase 4.1. IRB Protocol
of the study involved the analysis and synthesis of
The entire process adhered to ethical guidelines outlined
the data collected in phases 1 and 2. Quantitative
in the IRB protocols #5139 "Team Chase Undergraduate
data analyzed using descriptive and inferential
Research Volunteers (URV)" and #5358 "AI in Education
statistics, while qualitative data analyzed using
and Automata Theory: Synthesis Proposal".
content analysis. The results of the analysis syn-
thesized to identify best practices and potential
recommendations for future research and prac- 4.2. Study Testing
tice.
4.2.1. Consent Form
After beginning Phase 1, the author collaborated withBefore conducting interviews with educators, partici-
an undergraduate student on the Honors Thesis Projectpants were required to provide informed consent. The
committee to explore educatorsâ mental models and per-
consent form outlined the purpose of the semi-structured
spectives on how RLOs impact learning. Key research interview, which aimed to contribute to an honors thesis
questions included: exploring adaptive learning tools for future-generation
technologies within the realm of AI in educational
1. How does the RLO compare with traditional Reusable Learning Objects (RLOs). The research study
methods in understanding regular expressions? was designed to unpack the mental models surrounding
2. How does the RLO shape studentsâ problem- AI in education. Eligible participants for the study in-
solving skills? cluded instructors and teaching assistants specializing
3. Does RLO use deepen understanding of automata in areas such as proofs, induction, reason, number the-
principles? ory, automata theory, regular expressions, finite state
4. What is the connection between RLO engagement machines, and related courses. We informed participants
and CS250 performance? that the interview lasted approximately 20 minutes and
5. How do demographics correlate with learning primarily focused on soliciting their opinions and views
outcomes? regarding their experience with curriculum development
and AI in Education, as well as related research topics.
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
Figure 2: SWOT analysis pulled from the SWOT Analysis on AI in Education by Andre Kenneth Chase Randall [14]
The interview posed minimal risks such as potential fa- Table 1
tigue or boredom. We disclosed risks and encouraged to Description of our participants for semi structures interviews.
adhere to the 20-20-20 rule for eyestrain and to maintain Eligible participants for the study included instructors and teach-
hydration throughout the interview. The 20-20-20 rule ing assistants with selfdescribed expertise within the discrete
advises taking a 20-second break from looking at screens mathematics area such as proofs, induction, reason, number the-
every 20 minutes. During this break, focus on something ory, automata theory, regular expressions, finite state machines,
and related courses.
at least 20 feet away. This practice helps prevent eye
strain and fatigue caused by prolonged screen use. The Institution TA Instructors
consent form also covered data privacy, assuring par- University of Massachusetts Amherst 3 1
University of Pennsylvania 0 1
ticipants that personal information would be handled
University of California Berkeley 0 2
confidentially with the IRB protocols. Data collection
Cornell University 0 1
will be conducted anonymously with transcribed inter-
Total 3 5
views in the studyâs codebook. Communication on any
concerns was encouraged throughout the interview as
well. The consent form process and interview proto-
4.2.2. SEMI-STRUCTURED INTERVIEW SCRIPT
col were carefully designed to uphold ethical standards,
ensure participant confidentiality and comfort, and facil- We designed the questions to explore educatorsâ mental
itate valuable insights into participantsâ mental models models regarding their teaching experiences and perspec-
surrounding AI in education. tives on adaptive learning tools. Some questions were
adapted from the I-Corps customer discovery project to
ensure depth in understanding both practical and attitu-
dinal dimensions of their insights. The specific questions
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
Table 2 learning management systems, and reliance on teaching
Usability Testing Participants; 44 participants completed the assistants, were often mentioned as significant factors
consent forms, pre survey, and post survey. 65 students opted to influencing tool adoption.
use the alternative research for the option to get two extra credit This feedback forms a basis for refining adaptive learn-
points with 194 students opting not to partici ing tools to meet real-world teaching demands.
Group Number of Students
Treatment Group 44
Control Group 65 5. Conclusion and Future Work
Non-participants 194
Total 303 One of the most notable advantages of AI in education is
its ability to personalize learning, tailoring educational
experiences to meet the diverse needs, preferences, and
included are as follows: learning styles of students [15]. Using an RLO as the foun-
dation for intelligent tutoring systems could enhance the
1. Course Background: Educators describe their educational landscape by providing customized learning
teaching context. paths and tailored feedback [9]. Participant feedback in
2. Teaching Challenges: Identification of course- this study highlighted the RLOâs potential for support-
specific challenges. ing course material and fostering problem-solving skills,
3. Adaptive Solutions: Educators suggest solu- with Participant #2 noting, âYes, it was very useful in
tions to their teaching challenges. helping check work.â Participant #5 also recommended
4. Solution Drawbacks: Exploration of limitations the tool, sharing that âit was helpful in understanding
in current methods. how regex are set up.â
5. Design Preference: Educators explain why pre- Despite these benefits, participants expressed a desire
ferred designs are effective. for more personalized feedback. As Participant #6 re-
marked, âIt wasnât personalized, but it was useful,â under-
6. Teaching Assistant Roles: Understanding TA
scoring the need for individualized support within such
responsibilities in supporting adaptive tools.
tools. Suggestions for improvement, including âmore lan-
7. Adaptive Tools Perspective: Insights on poten-
guage optionsâ and âtext-to-speechâ functionality (Par-
tial and challenges of adaptive tools.
ticipant #3), point to the importance of accessibility and
8. Incorporation Process: Discuss practical steps adaptability in future RLO iterations. Additionally, feed-
for tool implementation. back from Participant #1, who stated, âI wasnât sure how
9. Tool Features and Functionality: Discuss ex- to use the tool at first,â indicates that enhancing user
pectations for tool features to aligned with teach- guidance could improve ease of use. Comments from
ing styles. Participant #15, who noted, âI would not recommend the
10. Professional Recommendations: Suggestions tool to others; it did not explain very much and took a
for other participants in adaptive learning studies. long time to run,â suggest areas for improving efficiency
11. Additional Insights: Educators provide and instructional depth.
thoughts on adaptive learning tools. Future work on this RLO aligns with the AI Grand
12. Open Feedback: Educators can add any other Challenges for Education outlined by Woolf et al., partic-
relevant thoughts. ularly the goals of providing "mentors for every learner"
and "lifelong and life-wide learning" [8]. In line with AIâs
From interview with instructors and teaching assis- strengths, weaknesses, opportunities, and threats in edu-
tants, we gain insight into teaching experiences, adaptive cation, as explored by Randall in recent discussions with
learning perspectives, and practical integration strate- HBCU faculty [? ], future RLO development will focus
gies. Generally, interviewees highlighted several com- on designing adaptable tools to support students from
mon themes: varied backgrounds, ensuring inclusivity, accessibility,
Challenge in Differentiating Instruction: Educa- and effectiveness. Planned enhancements include:
tors often noted difficulty in addressing the varying levels
within a single classroom and expressed interest in tools ⢠Adaptive Assessment and Skill Tracking: Im-
to support personalized pacing. plementing a system that tracks student perfor-
Positive Reception for Adaptive Tools: Many val- mance to provide tailored difficulty levels and
ued adaptive tools for their potential in enhancing con- additional exercises based on individual progress.
cept retention and student engagement, specifically when ⢠Dynamic Hints and Explanations: Adding
tailored to individual learning trajectories. context-aware hints that address specific errors,
Implementation Considerations: Practical con- such as syntax misunderstandings in regular ex-
straints, including time, ease of integration with current pressions, and provide tailored guidance.
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
⢠Immediate Corrective Feedback: Offering for- Research Volunteers), Jasmine Ngo assisted the deploy-
mative feedback that is specific to the type of ment of the semi structured interviews and the survey
error made, enabling students to correct misun- instruments as part of an undergraduate Computer Sci-
derstandings promptly. ence Department Honors Thesis Project.
⢠Personalized Learning Pathways: Creating Shravan Janga, served as part of Team Intelligent Tu-
customized learning pathways based on initial as- toring Systems R Us. He provided invaluable back up
sessments or adaptive quizzes, allowing students support during the customery discovery phase.
to focus on areas that require reinforcement. The author acknowledges partial funding from the
⢠Progress and Performance Dashboards: De- National GEM Consortium, the Intel Scholars Program,
veloping dashboards that give students personal- the NSF I-Corps Hub for Interior Northeast led by Cor-
ized insights into their strengths and areas need- nell Tech, Manning College of Information and Com-
ing improvement, supporting self-directed learn- puter Science along with the Spaulding- Smith Fellow-
ing. ship awarded by the UMass Graduate School.
⢠Natural Language Processing (NLP) for Open-
Ended Responses: Using NLP to analyze open-
ended responses and provide customized feed- 7. Appendices
back based on the semantics of studentsâ answers.
⢠Gamification and Motivational Feedback: In- 7.1. Appendix A: Automata Theory
corporating gamified elements that reward indi- Discussion Objectives
vidual achievements and keep students motivated Every week for the length of the course, students met
throughout their learning journey. for a 50 minutes discussion group to cover the following
⢠Student Profile Customization: Enabling stu- topics:
dents to set preferences or learning goals within
the RLO, allowing it to customize feedback based 1. âWhat is a Proof?â
on their unique needs and learning styles. ⢠Objective: To foster an understanding of
mathematical proof in real-world scenarios
These enhancements aim to make the RLO a more and to practice constructing proofs based
flexible, accessible, and impactful educational tool that on definitions, highlighting the role of pre-
supports a broad range of learners. By focusing on per- conditions, postconditions, and loop in-
sonalization and adapting to diverse learning needs, this variants in validating code.
RLO aligns with AIâs grand challenge to democratize ed- 2. "A Murder Mysteryâ
ucational resources and extend individualized learning
⢠Objective: To develop deductive reason-
opportunities beyond traditional settings.
ing skills using propositional logic, demon-
strating the process of narrowing down
6. Acknowledgments possibilities based on given clues, and ap-
plying rules of propositional logic to de-
The author thanks the study participants for their valu- duce conclusions efficiently.
able insights and time. Besides the study participants, 3. âPracticing Proofsâ
the author extends appreciation to several entities who ⢠Objective: To enhance proficiency in ap-
contributed to the development of this paper and its find- plying proof methods to statements about
ings. Research professor Beverly P. Woolf serves as his functions and relations, emphasizing pred-
research advisor. Professor Woolf specializes generally in icate proof rules and the significance of
educational computer science research and more specifi- properties of functions and relations.
cally in intelligent tutoring systems. Professor David A.
4. âInfinitely Many Primesâ
Mix Barrington, the authors teaching assistant supervisor
and synthesis reader, focuses on computational complex- ⢠Objective: To apply proof techniques and
ity, Boolean circuits, automata, and logic. The combined congruence principles to establish and
expertise of both professors bridges diverse fields, offer- understand facts about prime numbers,
ing a comprehensive perspective on the project. specifically the infinitude of primes.
Samuel Osebe and Sabrina Zaman Ishita assisted in 5. âPracticing Induction Proofsâ
RLO prototype development as part of a course team ⢠Objective: To cultivate a strong founda-
project. They both helped in crafting a proof of concept tion in mathematical induction, focusing
for the pilot project. on the structure of induction proofs includ-
Under the UMass Human Protect Office (Internal Re- ing base cases, inductive hypotheses, and
view Board Protocol #5139: Team Chase Undergraduate inductive steps.
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
6. âMore Induction Problemsâ Define the language đ¸đ¸đ (âeven-even-primitiveâ) of
⢠Objective: To reinforce and expand stu- nonempty strings that are in đ¸đ¸ and have no proper
dentsâ skills in mathematical induction, prefix in đ¸đ¸. (That is, if đ¤ â đ¸đ¸đ and đ¤ = đ˘đŁ with
challenging them with diverse problems both đ˘ and đŁ â đ¸đ¸, then either u = or v = .) It turns
that require careful proof construction. out that while đ¸đ¸đ is harder than đ¸đ¸ to describe in
English, it has a simpler regular expression.
7. âBoolean Expressionsâ
⢠Explain why đ¸đ¸ = (đ¸đ¸đ )
⢠Objective: To familiarize students with ⢠Which strings of up to six letters are in đ¸đ¸đ ?
Java-based boolean expressions, emphasiz- ⢠Construct a regular expression for đ¸đ¸đ , and explain
ing the differences between common pro- why this solves the main problem.
gramming languages and the structure of
tree representations in code.
8. âCourse Evaluation Essay Questionsâ
References
⢠Objective: To gather feedback on the [1] U. of Massachusetts Amherst, Compsci 250: Intro-
course content, pedagogy, and overall duction to discrete mathematics, n.d. URL: https:
learning experience, aiding in future im- //people.cs.umass.edu/~barring/cs250/, accessed:
provements and refinements. 2024-10-11.
9. âDesigning Regular Expressionsâ [2] D. A. Wiley, Connecting learning objects to in-
structional design theory: A definition, a metaphor,
⢠Objective: To master the art of construct-
and a taxonomy, in: The Instructional Use of
ing accurate regular expressions for speci-
Learning Objects, volume 2830, Agency for Instruc-
fied languages, promoting a systematic ap-
tional Technology and Association for Educational
proach to capture all desired strings while
Communications & Technology, Bloomington, In-
excluding undesired ones.
diana, 2002, pp. 1â35. URL: https://www.scirp.org/
10. âMinimizing a DFAâ reference/referencespapers?referenceid=589633.
⢠Objective: To comprehend the principles [3] P. R. Polsani, Use and abuse of reusable learning
behind the Myhill-Nerode Theorem, and to objects, in: E-education: Design and Evaluation,
acquire hands-on experience in minimiz- Learning Technology Center, University of Arizona,
ing DFAs by leveraging the equivalence 2003. URL: https://jodi-ojs-tdl.tdl.org/jodi/article/
classes of the relation on strings. view/jodi-105.
11. âApplications in Compilersâ [4] R. H. Kay, L. Knaack, Exploring the impact of
learning objects in secondary school mathemat-
⢠Objective: To understand the foundational
ics and science classrooms: A formative analysis,
role of deterministic finite automata in
Australasian Journal of Educational Technology
the lexical analysis phase of compilers,
28 (2012) 775â792. URL: https://eric.ed.gov/?id=
underscoring the transition from high-
EJ1073840.
level programming languages to machine-
[5] I. Douglas, Instructional design based on reusable
understandable code.
learning objects: applying lessons of object-
oriented software engineering to learning sys-
7.2. Appendix B: Discussion #8 on Regular tems design, in: 31st Annual Frontiers in Ed-
Expressions ucation Conference. Impact on Engineering and
Science Education. Conference Proceedings (Cat.
Writing Exercise: No.01CH37193), volume 3, 2001, pp. F4Eâ1. doi:10.
Construct a regular expression for the set EE ("even- 1109/FIE.2001.963968.
even") of strings in {đ, đ} that have both an even number [6] A. Staikopoulos, I. OKeeffe, B. Yousuf, O. Con-
of đⲠđ and an even number of đⲠđ . Justify your answer lan, E. Walsh, V. Wade, Enhancing stu-
carefully â explain why your expression generates only dent engagement through personalized motiva-
even-even strings and why it generates all even-even tions, in: ICALT, IEEE, Hualien, Taiwan,
strings. 2015, pp. 340â344. URL: https://ieeexplore.ieee.org/
Note that all even-even strings have even length, so abstract/document/7265345. doi:10.1109/ICALT.
you may think of the whole string as being broken up 2015.116.
into two-letter blocks. [7] B. P. Woolf, Ai and education: Celebrating 30 years
Here are some more hints. You are not required to use of marriage, in: B. Jesus, M. Kasia (Eds.), AIED
them to solve the main problem, but they will probably Workshops, volume 1432 of CEUR Workshop Pro-
be useful.
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Andre Kenneth Chase Randall CEUR Workshop Proceedings 1â9
ceedings, CEUR-WS.org, Madrid, Spain, 2015, pp.
38â47. URL: https://ceur-ws.org/Vol-1432/ai_ed_
pap5.pdf.
[8] B. Woolf, H. C. Lane, V. K. Chaudri, J. L. Kolodner,
Ai grand challenges for education, AI Magazine 34
(2013) 66â84. URL: https://ojs.aaai.org/aimagazine/
index.php/aimagazine/article/view/2490. doi:10.
1609/aimag.v34i4.2490.
[9] O. Gutierrez, A strategy for the development of
reusable learning objects in the boston area ad-
vanced technological education connections part-
nership, in: Issues in Information Systems (IIS), vol-
ume 4, International Association for Computer In-
formation Systems (IACIS), Las Vegas, 2003, pp. 143â
149. URL: https://iacis.org/iis/2003-!/Gutierrez.pdf.
[10] B. P. Woolf, Artificial Intelligence in Edu-
cation, John Wiley & Sons, Inc., 1992. URL:
https://web.cs.umass.edu/publication/docs/1991/
UM-CS-1991-037.pdf.
[11] S. Blank, J. Engel, The national science founda-
tion innovation corps⢠teaching handbook, 2018.
URL: https://venturewell.org/wp-content/uploads/
NSF_Handbook_Web.pdf.
[12] M. Weeks, D. Binnion, A. C. Randall, V. Patel, Ad-
venture game with a neural network controlled
non-playing character, 16th IEEE International
Conference on Machine Learning and Applications
(ICMLA) (2017). URL: https://ieeexplore.ieee.org/
abstract/document/8260663.
[13] C. E. Hub, Interior northeast i-corps hub,
2023. URL: https://eship.cornell.edu/item/
interior-northeast-i-corps-hub/, accessed: 2024-10-
11.
[14] A. K. C. Randall, Ai in education: A swot anal-
ysis with chase randall, 2024. URL: https://www.
youtube.com/watch?v=OsO9nFa5rvQ.
[15] I. Celik, M. Dindar, H. Muukkonen, The promises
and challenges of artificial intelligence for teach-
ers: A systematic review of research, TechTrends
66 (2022) 616â630. URL: https://doi.org/10.1007/
s11528-022-00715-y.
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