Predicting student performance on post-requisite skills using prerequisite skill data

Adjei, Seth; Botelho, Anthony F.; Heffernan, Neil T.

doi:10.1145/2883851.2883867

Cited by 17 publications

(6 citation statements)

References 4 publications

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“…Data from prerequisite skills is used to determine how well students do in post-requisite skills. This aids in honing the pre-requisite abilities needed to learn the post-requisite skills [18].…”

Section: Literature Surveymentioning

confidence: 99%

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A Performance Analysis of the Impact of PriorKnowledge on Computational Thinking

Navandar¹,

Kiwelekar²,

Laddha³

2023

IJMECS

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Previously acquired knowledge plays a significant role to learn new knowledge and skills. Previously acquired knowledge consists of Short-term memory and Long-term memory. Though it is a well-accepted learning phenomenon, it is challenging to empirically analyse the impact of prior knowledge on learning. In this paper, we use two systems models for human thinking proposed by Nobel Laureate Prof. Daniel Kahneman. This is a model for human cognition which uses two systems of thinking-the first being quick and intuitively known as fast thinking and the second being slow and tedious known as slow thinking. While slow thinking uses long-term memory, fast thinking uses short-term memory. The impact of prior knowledge of programming language is analyzed to learn a new programming language. We assigned a learning task to two different groups with one having learnt a programming language i.e. senior students and the second group without any prior knowledge of programming language i.e. freshers. The impact of prior knowledge is measured and compared against the time taken to answer quizzes.

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Section: Literature Surveymentioning

confidence: 99%

“…Using prerequisite skill traits to predict retention performance [17] 3 Linear Regression Using prerequisite skills data to predict student success on post-requisite skills [18] 4…”

Section: Correlationmentioning

confidence: 99%

A Performance Analysis of the Impact of PriorKnowledge on Computational Thinking

Navandar¹,

Kiwelekar²,

Laddha³

2023

IJMECS

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“…Courses with no prerequisite weight are not prerequisites for any courses and courses with less prerequisite weight are prerequisites for fewer courses. Since the post requisite courses are more difficult than prerequisite courses [34,35], a better sequencing has to be recommended to balance the difficulty index throughout the degree program. MPW gives higher priority for prerequisite weight (pwi) in which courses with more pwi are distributed in the early semesters and less pwi are distributed in the later semesters.…”

Section: Algorithm 2 Mpw Algorithm Step 1 Let Picked_courses[x] = {ø}mentioning

confidence: 99%

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Premalatha

Viswanathan

2019

Cybernetics and Information Technologies

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Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

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“…As part of these efforts, sensor data [9,10], online learning data [11] and teaching data [9] have been widely used to tackle a variety of problems and challenges for the learning analytics, including learner modelling [12], collaborative learning [13], teaching analytics [14] and privacy issues [15]. For example, Fidalgo et al [16] utilize student online data to improve teamwork assessment; Guia et al [17] use wearable device data to assist language learning for young children.…”

Section: Related Workmentioning

confidence: 99%

“…Taking advantage of the vast amounts of data generated from heterogeneous sources in the educational space, learning analytics [ 1 ] has become a fast-growing field in recent years, which mainly focuses on understanding and analysis of data generated during the learning process [ 8 ]. As part of these efforts, sensor data [ 9 , 10 ], online learning data [ 11 ] and teaching data [ 9 ] have been widely used to tackle a variety of problems and challenges for the learning analytics, including learner modelling [ 12 ], collaborative learning [ 13 ], teaching analytics [ 14 ] and privacy issues [ 15 ]. For example, Fidalgo et al [ 16 ] utilize student online data to improve teamwork assessment; Guia et al [ 17 ] use wearable device data to assist language learning for young children.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Learning Analytics Using Commodity Wearable Devices

Zhang

et al. 2017

Sensors

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We advocate for and introduce LEARNSense, a framework for learning analytics using commodity wearable devices to capture learner’s physical actions and accordingly infer learner context (e.g., student activities and engagement status in class). Our work is motivated by the observations that: (a) the fine-grained individual-specific learner actions are crucial to understand learners and their context information; (b) sensor data available on the latest wearable devices (e.g., wrist-worn and eye wear devices) can effectively recognize learner actions and help to infer learner context information; (c) the commodity wearable devices that are widely available on the market can provide a hassle-free and non-intrusive solution. Following the above observations and under the proposed framework, we design and implement a sensor-based learner context collector running on the wearable devices. The latest data mining and sensor data processing techniques are employed to detect different types of learner actions and context information. Furthermore, we detail all of the above efforts by offering a novel and exemplary use case: it successfully provides the accurate detection of student actions and infers the student engagement states in class. The specifically designed learner context collector has been implemented on the commodity wrist-worn device. Based on the collected and inferred learner information, the novel intervention and incentivizing feedback are introduced into the system service. Finally, a comprehensive evaluation with the real-world experiments, surveys and interviews demonstrates the effectiveness and impact of the proposed framework and this use case. The F1 score for the student action classification tasks achieve 0.9, and the system can effectively differentiate the defined three learner states. Finally, the survey results show that the learners are satisfied with the use of our system (mean score of 3.7 with a standard deviation of 0.55).

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Predicting student performance on post-requisite skills using prerequisite skill data

Cited by 17 publications

References 4 publications

A Performance Analysis of the Impact of PriorKnowledge on Computational Thinking

A Performance Analysis of the Impact of PriorKnowledge on Computational Thinking

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

A Framework for Learning Analytics Using Commodity Wearable Devices

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