Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling

Yao, Mengfan; Zhao, Siqian; Sahebi, Shaghayegh; Feyzi-Behnagh, Reza

doi:10.1145/3442381.3450104

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…In recent years neural Marked Temporal Point Processes (MTPP) have shown a significant promise in modeling a variety of continuoustime sequences in healthcare [12,33], finance [3,47], education [43], and social networks [9,11,23,29,48,49]. However, due to the limitations of traditional MTPP models, in recent years, neural enhancements to MTPP models have significantly enhanced the predictive power of these models.…”

Section: Temporal Point Processesmentioning

confidence: 99%

“…In recent years, neural marked temporal point processes (MTPP) have shown a significant promise in modeling a variety of continuoustime sequences in healthcare [33,34], finance [3,47], education [43], and social networks [29,48,49]. However, standard MTPP have a limited modeling ability for CTAS as: (i) they assume a homogeneity among sequences, i.e., they cannot distinguish between two sequences of similar actions but with different time duration; (ii) in a CTAS, an action may finish before the start of the next action and thus, to model this empty time interval an MTPP must introduce a new action type, i.e., NULL or end-action which may lead to an unwarranted increase in the types of actions to be modeled; and (iii) they cannot encapsulate the additional features associated with an action, for e.g., minimum time for completion, necessary previous actions, or can be extended to sequence generation.…”

Section: Introductionmentioning

confidence: 99%

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ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences

Gupta,

Bedathur

2022

Preprint

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Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike machine-made time series, these action sequences are highly disparate as the time taken to finish a similar action might vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, etc. Existing neural approaches that model an activity sequence are either limited to visual data or are taskspecific, i.e., limited to next action or goal prediction. In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an early detection of sequence goal via a constrained margin-based optimization procedure. This in-turn allows ProActive to predict the sequence goal using a limited number of actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of ProActive over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation. CCS CONCEPTS• Information systems → Data mining.

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Section: Temporal Point Processesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences

Gupta,

Bedathur

2022

Preprint

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“…These estimates can be used to inform personalization, either by i) providing feedback to help teachers and instructors monitor student progress and intervene if necessary or ii) providing personalized learning activity recommendations directly through digital learning platforms. There exist a wide range of student models, from those that analyze student responses to questions, such as item response theory [24] and models for knowledge tracing [9], to those that analyze student activity within digital learning platforms [4,28,30].…”

Section: Introductionmentioning

confidence: 99%

Process-BERT: A Framework for Representation Learning on Educational Process Data

Alexander¹,

Brinton²,

Lan³

2022

Preprint

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Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention. However, analyzing process data is challenging since the specific format of process data varies a lot depending on different learning/testing scenarios. In this paper, we propose a framework for learning representations of educational process data that is applicable across many different learning scenarios. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data and a fine-tuning step that further adjusts these representations on downstream prediction tasks. We apply our framework to the 2019 nation's report card data mining competition dataset that consists of student problem-solving process data and detail the specific models we use in this scenario. We conduct both quantitative and qualitative experiments to show that our framework results in process data representations that are both predictive and informative. 1

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