Learning Problems Research Articles

Adaptation and validation of the children’s cognitive triad inventory for Indonesian students

Depression causes student learning problems. Depressive symptoms are influenced by automatic thoughts that contain a negative cognitive triad. A valid and reliable instrument to measure the cognitive triad in adolescents is essential to identify one of the risk factors for depressive disorders in adolescents. At the same time, Indonesia has no instrument to measure this. This study aims to adapt the children’s cognitive triad inventory (CTI-C) into an Indonesian version and to examine its psychometric properties. Data were collected from 1,184 students aged 13-19 (mean=15.32, SD=1.62) using the 36-item CTI-C. The CTI-C instrument adaptation process refers to the international test commission (ITC). The results of confirmatory factor analysis (CFA) showed comparative fit index (CFI)=0.95, Tucker-Lewis’s index (TLI)=0.94, root mean square error of approximation (RMSEA)=0.071, standardized root mean square residual (SRMR)=0.056, and goodness of fit index (GFI)=0.82, with a load factor range of 0.43-0.80 for the view of self, 0.43-0.68 for the view of the world, and 0.37-0.70 for the view of the future. The Cronbach’s alpha coefficient ranged from 0.81 to 0.84 for the subscales and 0.93 for the total score. Thus, the Indonesian version of the CTI-C can measure the cognitive triad in Indonesian students aged 13-19.

Developing a growth mindset in education: a bibliometric analysis and its challenge during pandemic

The growth mindset is essential for learning because students encounter numerous obstacles during the pandemic. However, only a few studies have examined research trends in growth mindset pedagogy. In filling this gap, this study aims to conduct a bibliometric study using the growth mindset pedagogy during the pandemic. Data from 81 Scopus-sourced scholarly articles on growth mindset in education from 2020–2022 was used. VOSviewer qualitatively analyzed the data. Results showed that the research cluster’s growth mindset is diverse and adaptive to learning dynamics, notably during the COVID-19 pandemic. Several research clusters examined instructors, students, and parents’ roles in learning. Another cluster focuses on academic achievement and teacher efforts to improve student performance. The research addresses student issues like depression, academic fatigue, and suicide ideation. Research on a growth mindset in education has grown quantitatively since 2021. Clusters 1, 2, and 4, 5 have the most research on active learning design to improve student academic performance and accomplishment through teachers and other supporting elements. Even though academic fatigue can lead to depression and suicidal ideation, research on academic achievement-related student issues is scarce. In conclusion, a growth mindset can significantly enhance academic performance and solve learning problems.

"Intelligent tutoring effects on induced emotions and cognitive load of learning-disabled learners".

This study addresses the learning requirements of learners with learning difficulties by monitoring their learning experience in an Intelligent Tutoring System. Intelligent Tutoring Systems were developed to enrich the teaching-learning process. In the present work, the interface is designed and developed utilizing the potential of Artificial Intelligence to meet their individual needs. Designing an online learning platform for a learners with learning difficulties requires consideration of their learning problems and preferences. The interface was developed focusing on all the requirements of the LD learners. The objective of the present study is to monitor the learning experience in the form of induced emotions and cognitive load of the learners to determine the impact of learning. 83 learners were observed during various stage of learning. The results obtained through the Support Vector machine (SVM) classification technique showed the positive attitude towards intelligent tutoring. The analysis revealed that a total of 0.23% of learners were positively induced. Their learning experience was positive and effective. The cognition load on learners was minimum with single-mode instruction and least disturbed. The system was improved based on preference feedback on design features. This helps in improving content design and creating device independent and responsive visual design. The fatigue effect analysis on cognitive load implied that multiple modes of instruction increased drowsiness. Single mode of instruction have a positive impact on the learning process and it reduces the cognitive load of the learners.Implications for RehabilitationThe user interface designed and developed for learners with Dyslexia, Dysgraphia and Dyscalculia has learning disabled-friendly features. These can be used to create a device-independent and responsive design.Learning experience is monitored along with the impact on cognitive load of the learners.The research helps in understanding the stimulation and response of learners with learning disability for different learning conditions.Most existing learning systems are limited to non-learning-disabled learners. The ITS developed during research presents a Universal learning design helpful for all learners with and without learning disability.

Education Model for Paediatric Palliative Care

Introduction: Meeting the palliative care needs of children and their families is complex and challenging. For countries such as Australia, whose relatively small paediatric palliative care population is dispersed across a very large geographical area, one challenge is maintaining a skilled workforce in regional, rural, and remote areas, where, when compared to major cities, there are fewer resources, and the workforce is often transient. Methods: The Quality of Care Collaborative Australia (QuoCCA) Pop-up Model of Education was used to provide palliative care education to health professionals across three geographical locations and facilities, to facilitate a 2,500-km transfer of a child with complex palliative care needs from a tertiary hospital to the remote family home on Country. Results: Each Pop-Up provided effective education to facilitate the successful transfer of the child to the next hospital location. Over 18 months, three Pop-Ups occurred. Relational learning and real-time problem solving enabled health professionals to build confidence and capacity to successfully transfer the child from the regional hospital to the remote family home. Implications: The QuoCCA Education Pop-up Model is a feasible method to deliver timely access to speciality education. The model can be successfully applied in multiple settings. Keywords: paediatric, palliative care, integrated health care delivery, learning, rural, education models.

Ship-Radiated Noise Separation in Underwater Acoustic Environments Using a Deep Time-Domain Network

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Trustworthy Precision Medicine: An Interpretable Approach to Detecting Anomalous Behavior of IoT Devices.

The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine-learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach.

Improving Thinking Skills Through Usul Al-Fiqh Learning: An Action Research on Prospective Islamic Religious Education Teachers

As part of the study of Islamic law, ushul fiqh demands that one be able to practically handle religious issues by using the ulama's defined principles of Islamic law. Applying Islamic legal theory practically calls for critical thinking abilities. Therefore, this research aims to find a learning formulation in Ushul fiqh that integrates efforts to improve thinking skills. To achieve this goal, action research was chosen as a research design which was carried out in three research stages, namely pre action, implementation action and post action. This design was chosen considering that the context of this research is oriented towards finding solutions to learning problems. This research involved 35 prospective Islamic religious education teachers who took Ushul Fiqh courses from one of the government-owned Islamic universities, which is quite popular in Indonesia. Data was collected through surveys and semi-structured interviews which were analyzed using a descriptive quantitative approach and qualitative with content analysis. This research produced seven stages as learning formulations that need to be implemented in the ushul fiqh learning process. The study concludes that the proposed seven steps effectively elevate students' thinking skills to the Higher Order Thinking Skills (HOTS) level while applying usul al-fiqh principles. The findings suggest a promising pedagogical approach for cultivating advanced thinking skills within the context of Islamic law education. The main contribution of this study is to present a practical methodological guide regarding the steps for teaching usul fiqh, which is oriented toward improving students' thinking skills.

Sparse Approximate Pseudoinverse Preconditioning for Sparse Supervised Learning Problems with More Features than Samples

In this paper, we propose a new technique for handling a class of sparse supervised learning problems, specifically focusing on regression, binary, and multi-class classification problems. Sparse regression problems are associated with datasets including both arithmetic and categorical features and by encoding the dataset leads to an underdetermined sparse linear system. Furthermore, logistic regression can be used for both sparse binary and multi-class classification problems, solving an underdetermined sparse linear system. A new technique called Sparse Approximate Pseudoinverse Preconditioning (SAPP), namely the Explicit Preconditioned Conjugate Gradient for Normal Equations (EPCGNE) method based on Generic Approximate Sparse Pseudoinverse matrices is introduced for solving underdetermined sparse least square problems. Numerical experiments were carried out demonstrating a significant improvement of the performance metrics for the proposed SAPP scheme compared to other learners.

Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

AbstractThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra‐high‐performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO‐CatBoost, DMO‐CatBoost, and ASO‐CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the “black box” problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO‐CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R2, MAPE, and MAE values of the DMO‐CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

AbstractThe challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques.

English Language Teaching and Learning in Secondary Schools in Isoko North Local Government Area of Delta State: Problems and Prospects

This paper studied the problems of teaching and learning English as a second language in junior school certificate examination in Isoko North Local Government Area of Delta State. The study sought to establish the extent to which students in Junior Secondary School and teachers should value English as the language of instruction. The framework adopted is the behaviorist theory of bilingualism. The simple percentage method of data analysis was employed to analyze data. The population comprised students in junior secondary schools in Ozoro precisely the students of Opute Grammar School, Ozoro and Heroes Group of Schools, Ozoro both in Delta State of Nigeria. The sample size was 100, and the instrument for data collection was the questionnaire which was tested and retested on some other students to ensure that it was reliable. It was found out that the majority of the students consider the English language a difficult subject because of their inability to understand every aspect, which the English language entails. It was also found out among others that the level of infiltration of mother tongue into the respondents’ English is very high. This research recommended that good reading habits should be developed in the students and also well-equipped libraries should be provided in the schools and communities in order to improve better performance in their study of English language. It concluded that the problems of teaching and learning English as a second language is as a result of the impact of mother’s tongue, inadequate textbooks, poor language background and lack of professional growth and development of teachers. If these discovered factors are not checked there will be a drastic decline in standard of education.

Robust Parallel Pursuit for Large-Scale Association Network Learning

Sparse reduced-rank regression is an important tool to uncover the large-scale response-predictor association network, as exemplified by modern applications such as the diffusion networks, and recommendation systems. However, the association networks recovered by existing methods are either sensitive to outliers or not scalable under the big data setup. In this paper, we propose a new statistical learning method called robust parallel pursuit (ROP) for joint estimation and outlier detection in large-scale response-predictor association network analysis. The proposed method is scalable in that it transforms the original large-scale network learning problem into a set of sparse unit-rank estimations via factor analysis, thus facilitating an effective parallel pursuit algorithm. Furthermore, we provide comprehensive theoretical guarantees including consistency in parameter estimation, rank selection, and outlier detection, and we conduct an inference procedure to quantify the uncertainty of existence of outliers. Extensive simulation studies and two real-data analyses demonstrate the effectiveness and the scalability of the suggested approach. History: Accepted by Ram Ramesh, Area Editor/Data Science & Machine Learning. Funding: This work was supported by the National Key R&D Program of China [Grant 2022YFA1008000], Natural Science Foundation of China [Grants 72071187, 72091212, 71731010, and 71921001], China Postdoctoral Science Foundation [Grant 2023M733402], and Fundamental Research Funds for the Central Universities [Grants WK3470000017, WK2040000027, and WK2040000079]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0181 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0181 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Remediation of Learning Difficulty Utilizing School-Based Cognitive Behavioral Intervention Measured by EEG Theta-Alpha and Theta-Beta Ratio During Resting and Cognitive Task Performance Conditions.

Background. EEG is an effective tool due to its ability to capture and interpret the changes in brain activity under different situations. Quantitative EEG (qEEG) can be essential in evaluating and treating children's learning problems. Methods and procedure. Fifty school-going children with difficulty in learning were studied. Analysis of the difference between pre-intervention and postintervention EEG power ratio of frequency bands, including Theta to Beta and Theta to Alpha, while eyes-closed, eyes-open, hyperventilation, writing, and reading conditions and the values for relative powers were calculated. The study correlated postintervention theta/beta ratio (TBR) and theta/alpha ratio (TAR) values with behavioral measures. Results. The findings suggested that there was a significant difference in pre-intervention and postintervention relative TAR and TBR power values. A significant increase of TAR and TBR power values was observed in eyes-closed (resting), hyperventilation, writing, and reading task conditions, indicative of a state of arousal at FP1FP2, T3T4, and O1O2 scalp locations. During eye open conditions, the TAR and TBR were significantly low at all 3 scalp locations, indicating a relaxed, conscious, and aware state of mind. Postintervention TAR and TBR values were significantly correlated with behavioral measures during 5 task conditions on several scalp locations. Conclusion. These quantitative electroencephalogram findings in children with learning problems indicate that with the increased complexity of the cognitive tasks, TAR and TBR increase, while postintervention, children could attain a relaxed and conscious state of mind during eyes-open condition.

SHARP (SHort Answer, Rationale Provision): A New Item Format to Assess Clinical Reasoning.

Many non-workplace-based assessments do not provide good evidence of a learner's problem representation or ability to provide a rationale for a clinical decision they have made. Exceptions include assessment formats that require resource-intensive administration and scoring. This article reports on research efforts toward building a scalable non-workplace-based assessment format that was specifically developed to capture evidence of a learner's ability to provide a justification for a clinical decision that they had made. The authors developed a 2-step item format called SHARP (SHort Answer, Rationale Provision), referring to the 2 tasks that comprise the item. In collaboration with physician-educators, the authors integrated short-answer questions into a patient medical record-based item starting in October 2021 and arrived at an innovative item format in December 2021. In this format, a test-taker interprets patient medical record data to make a clinical decision, types in their response, and pinpoints medical record details that justify their answers. In January 2022, a total of 177 fourth-year medical students, representing 20 U.S. medical schools, completed 35 SHARP items in a proof-of-concept study. Primary outcomes were item timing, difficulty, reliability, and scoring ease. There was substantial variability in item difficulty, with the average item answered correctly by 44% of students (range, 4%-76%). The estimated reliability (Cronbach α) of the set of SHARP items was 0.76 (95% CI, 0.70-0.80). Item scoring is fully automated, minimizing resource requirements. A larger study is planned to gather additional validity evidence about the item format. This study will allow comparisons between performance on SHARP items and other examinations, the examination of group differences in performance, and possible use cases for formative assessment purposes. Cognitive interviews are also planned to better understand the thought processes of medical students as they work through the SHARP items.

From diagnosis to management: current perspectives on congenital cytomegalovirus infection.

Congenital CMV (cCMV) infection is the most common infection of newborns and a leading cause of hearing loss and other neurologic disabilities in children. This review focuses on the diagnosis, presentation and management of cCMV infection. Cytomegalovirus is one of the leading causes of sensorineural hearing loss in children. It also leads to neurodevelopmental disabilities and learning problems throughout childhood in both symptomatic and asymptomatic newborns. Urine and saliva PCR testing are the preferred methods of testing newborn infants for cCMV. In recent years, newborn-targeted and universal screening programs have been implemented in several states and major medical centers with the goal of identifying infected infants at risk for hearing loss. Treatment for infants diagnosed with cCMV infection should be limited to those who are moderately to severely symptomatic at birth with cCMV infection, though treatment may be beneficial for children who are asymptomatic with isolated sensorineural hearing loss. As more children with cCMV are being identified through newborn screening, understanding the clinical presentation and sequelae is important for appropriate management of children with cCMV.

A game theoretic decision forest for feature selection and classification

Abstract Classification and feature selection are two of the most intertwined problems in machine learning. Decision trees (DTs) are straightforward models that address these problems offering also the advantage of explainability. However, solutions that are based on them are either tailored for the problem they solve or their performance is dependent on the split criterion used. A game-theoretic decision forest model is proposed to approach both issues. DTs in the forest use a splitting mechanism based on the Nash equilibrium concept. A feature importance measure is computed after each tree is built. The selection of features for the next trees is based on the information provided by this measure. To make predictions, training data is aggregated from all leaves that contain the data tested, and logistic regression is further used. Numerical experiments illustrate the efficiency of the approach. A real data example that studies country income groups and world development indicators using the proposed approach is presented.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Transient dataset of household appliances with Intensive switching events

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing

Pre-operative prediction of post-surgical recovery for patients is vital for clinical decision-making and personalized treatments, especially with lumbar spine surgery, where patients exhibit highly heterogeneous outcomes. Existing predictive tools mainly rely on traditional Patient-Reported Outcome Measures (PROMs), which fail to capture the long-term dynamics of patient conditions before the surgery. Moreover, existing studies focus on predicting a single surgical outcome. However, recovery from spine surgery is multi-dimensional, including multiple distinctive but interrelated outcomes, such as pain interference, physical function, and quality of recovery. In recent years, the emergence of smartphones and wearable devices has presented new opportunities to capture longitudinal and dynamic information regarding patients' conditions outside the hospital. This paper proposes a novel machine learning approach, Multi-Modal Multi-Task Learning (M3TL), using smartphones and wristbands to predict multiple surgical outcomes after lumbar spine surgeries. We formulate the prediction of pain interference, physical function, and quality of recovery as a multi-task learning (MTL) problem. We leverage multi-modal data to capture the static and dynamic characteristics of patients, including (1) traditional features from PROMs and Electronic Health Records (EHR), (2) Ecological Momentary Assessment (EMA) collected from smartphones, and (3) sensing data from wristbands. Moreover, we introduce new features derived from the correlation of EMA and wearable features measured within the same time frame, effectively enhancing predictive performance by capturing the interdependencies between the two data modalities. Our model interpretation uncovers the complementary nature of the different data modalities and their distinctive contributions toward multiple surgical outcomes. Furthermore, through individualized decision analysis, our model identifies personal high risk factors to aid clinical decision making and approach personalized treatments. In a clinical study involving 122 patients undergoing lumbar spine surgery, our M3TL model outperforms a diverse set of baseline methods in predictive performance, demonstrating the value of integrating multi-modal data and learning from multiple surgical outcomes. This work contributes to advancing personalized peri-operative care with accurate pre-operative predictions of multi-dimensional outcomes.