基于学习者知识和性格的个性化课程推荐

doi:10.3969/j.issn.1000-5641.2022.06.010

摘要/Abstract

摘要：

自适应学习是使用计算机算法来协调自适应学习平台与学习者的互动, 并提供定制学习资源和学习活动来解决每位学习者的独特需求的教育方法. 自适应学习随着新冠疫情的影响变得越来越重要, 它面临的主要挑战之一是如何为学习者提供定制的学习资源, 即如何在海量的学习资源中为学习者生成个性化的推荐. 现存的方法大多使用基于学习者知识级别的推荐, 然而这些方法存在一些不足. 首先, 在获取学习者知识级别时, 学习者在学习过程中出现的遗忘现象并未得到很好的建模. 其次, 在推荐时, 这些方法将获取学习者知识级别的知识追踪技术和推荐分开进行, 忽视了这两者之间的深层连接关系. 此外, 仅考虑学习者的知识来建模, 将会忽略学习者对于学习资源类型、学习策略等的偏好. 为解决上述问题, 给出了一个知识和性格结合的多任务学习框架(Knowledge and Personality Incorporated Multi-Task Learning Framework, KPM)去促进课程 (即上文所指的学习资源) 的推荐, 该框架将增强的知识追踪任务作为辅助任务去协助主要的课程推荐任务. 具体地, 在增强的知识追踪任务(Enhanced Knowledge Tracing Task, EKTT)中, 设计了一个个性化的遗忘控制器去增强深度知识追踪模型, 从而更加准确地获取学习者的知识级别. 在课程推荐任务(Course Recommendation Task, CRT)中, 首先, 将学习者的知识级别、学习者的序列行为和学习者的性格根据特定的上下文进行自适应融合, 用以生成学习者的画像; 然后, 基于学习者的画像生成课程推荐列表. 在真实的教育相关数据集上的实验结果验证了提出的方法在点击率(Hit Ratio, HR)、归一化折损累计增益(Normalized Discounted Cumulative Gain, NDCG)、精确度(Precision)指标上的优越性, 即可以生成更加个性化的课程推荐.

关键词: 课程推荐, 知识追踪, 多任务学习, 性格, 自适应学习

Abstract:

Adaptive learning is an educational method that uses computer algorithms to coordinate interaction with learners, and provides customized learning resources and learning activities to address the unique needs of each learner. With the impact of COVID-19, adaptive learning has become increasingly important. One of the challenges with adaptive learning is how to provide personalized learning resources for learners—i.e., how to generate personalized recommendation for learners from a large set of learning resources. Existing methodologies mainly generate recommendations based on a learner’s knowledge level; however, this approach has some limitations. Firstly, when assessing a learner’s knowledge level, learners’ forgetting phenomenon has to date not been well modeled. Secondly, recommendations are generated separately from knowledge tracing tasks, ignoring the interconnectedness between these aspects. In addition, learners’ preferences for the type of learning resources and learning strategies is normally ignored if the knowledge level alone is used. To solve the aforementioned problems, this paper proposes a knowledge and personality incorporated multi-task learning framework (KPM) to boost course recommendations (i.e., the above-mentioned learning resources); the proposed method regards an enhanced knowledge tracing task (EKTT) as an auxiliary task to assist the primary course recommendation task (CRT). Specifically, using EKTT, we design a personalized forgetting controller to enhance the deep knowledge tracing model for accurately assessing a learner’s knowledge level. With CRT, we combine the learner’s knowledge level and sequential behavior with their personality adapted to the specific context to obtain learner’s profile; this data is subsequently used to generate a course recommendation list. Experimental results on real-world educational datasets demonstrate the superiority of our proposed method in terms of hit ratio (HR), normalized discounted cumulative gain (NDCG), and precision, indicating that our method can generate more personalized recommendations.

Key words: course recommendation, knowledge tracing, multi-task learning, personality, adaptive learning

中图分类号:

TP181

班启敏, 吴雯, 胡文心, 林晖, 郑巍, 贺樑. 基于学习者知识和性格的个性化课程推荐[J]. 华东师范大学学报（自然科学版）, 2022, 2022(6): 87-101.

Qimin BAN, Wen WU, Wenxin HU, Hui LIN, Wei ZHENG, Liang HE. Personalized course recommendations based on a learner’s knowledge and personality[J]. Journal of East China Normal University(Natural Science), 2022, 2022(6): 87-101.

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参考文献 37

1	KAPLAN A. Higher Education at the Crossroads of Disruption: The University of the 21st Century [M]. [S. l]: Emerald Group Publishing, 2021.
2	CARBONELL J R. AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 1970, 11 (4): 190- 202.
3	HUO Y J, WONG D F, NI L M, et al. Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation. Information Sciences, 2020, (523): 266- 278.
4	LIU Q, TONG S, LIU C, et al. Exploiting cognitive structure for adaptive learning [C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019: 627-635. DOI: 10.1145/3292500.3330922.
5	WU Z, LI M, TANG Y, et al. Exercise recommendation based on knowledge concept prediction. Knowledge-Based Systems, 2020, 210, 106481.
6	EBBINGHAUS H. Memory: A contribution to experimental psychology. Annals of Neurosciences, 2013, 20 (4): 155- 156.
7	HOCHREITER S, SCHMIDHUBER J. Long short-term memory. Neural Computation, 1997, 9 (8): 1735- 1780.
8	ZHANG Y, YANG Q. A survey on multi-task learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2021. DOI: 10.1109/TKDE.2021.3070203.
9	DIGMAN J M. Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 1990, 41 (1): 417- 440.
10	FURNHAM A. Personality and learning style: A study of three instruments. Personality and Individual Differences, 1992, 13 (4): 429- 438.
11	MOLLER L, SOLES C. Myers Briggs type preferences in distance learning education [J]. International Journal of Educational Technology, 2001, 2(2).
12	CHAMORRO-PREMUZIC T, FURNHAM A, LEWIS M. Personality and approaches to learning predict preference for different teaching methods. Learning and Individual Differences, 2007, 17 (3): 241- 250.
13	WU W, CHEN L, YANG Q C, et al. Inferring students’ personality from their communication behavior in Web-based learning systems. International Journal of Artificial Intelligence in Education, 2019, 29 (2): 189- 216.
14	ABYAA A, IDRISSI M K, BENNANI S. Learner modelling: Systematic review of the literature from the last 5 years. Educational Technology Research and Development, 2019, 67 (5): 1105- 1143.
15	GONG J, WANG S, WANG J, et al. Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view [C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 79-88. DOI: 10.1145/3397271.3401057.
16	PANDEY S, LAN A, KARYPIS G, et al. Learning student interest trajectory for MOOC thread recommendation [C]//Proceedings of the 20th International Conference on Data Mining Workshops. IEEE, 2020: 400-407. DOI: 10.1109/ICDMW51313.2020.00062.
17	刘恒宇, 张天成, 武培文, 等. 知识追踪综述. 华东师范大学学报(自然科学版), 2019, (5): 1- 15.
18	PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing. Advances in Neural Information Processing Systems, 2015, 28, 505- 513.
19	NAGATANI K, ZHANG Q, SATO M, et al. Augmenting Knowledge Tracing by Considering Forgetting Behavior [C]//Proceedings of the 28th International Conference on World Wide Web. 2019: 3101-3107. DOI: 10.1145/3308558.3313565.
20	GHOSH A, HEFFERNAN N, LAN A S. Context-aware attentive knowledge tracing [C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020: 2330-2339. DOI: 10.1145/3394486.3403282.
21	PANDEY S, SRIVASTAVA J. RKT: Relation-aware self-attention for knowledge tracing [C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2020: 1205-1214. DOI: 10.1145/3340531.3411994.
22	HADASH G, SHALOM O S, OSADCHY R. Rank and rate: Multi-task learning for recommender systems [C]//Proceedings of the 12th ACM Conference on Recommender Systems. 2018: 451-454. DOI: 10.1145/3240323.3240406.
23	CHEN Z, WANG X, XIE X, et al. Co-attentive multi-task learning for explainable recommendation [C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019: 2137-2143. DOI: 10.24963/ijcai.2019/296.
24	MENG W, YANG D, XIAO Y. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation [C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020 : 1091-1100. DOI: 10.1145/3397271.3401098.
25	WANG H, ZHANG F, ZHAO M, et al. Multi-task feature learning for knowledge graph enhanced recommendation [C]//Proceedings of the 28th International Conference on World Wide Web. 2019: 2000-2010. DOI: 10.1145/3308558.3313411.
26	GOSLING S D, RENTFROW P J, SWANN J W B. A very brief measure of the big-five personality domains. Journal of Research in Personality, 2003, 37 (6): 504- 528.
27	WU W, CHEN L. Implicit acquisition of user personality for augmenting movie recommendations [C]//Proceedings of the 23rd International Conference on User Modeling, Adaptation, and Personalization. Cham: Springer, 2015: 302-314. DOI: 10.1007/978-3-319-20267-9_25.
28	SHEN T, JIA J, LI Y, et al. Peia: Personality and emotion integrated attentive model for music recommendation on social media platforms [C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 34(01): 206-213. DOI: 10.1609/aaai.v34i01.5352.
29	YU Z, LIAN J, MAHMOODY A, et al. Adaptive user modeling with long and short-term preferences for personalized recommendation [C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019: 4213-4219. DOI: 10.24963/ijcai.2019/585.
30	ZHOU G, MOU N, FAN Y, et al. Deep interest evolution network for click-through rate prediction [C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 33(01): 5941-5948. DOI: 10.1609/aaai.v33i01.33015941.
31	BEUTEL A, COVINGTON P, JAIN S, et al. Latent cross: Making use of context in recurrent recommender systems [C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018: 46-54. DOI: 10.1145/3159652.3159727.
32	RENDLE S. Factorization machines with libFM [J]. ACM Transactions on Intelligent Systems and Technology , 2012, 3(3): Article number 57. DOI 10.1145/2168752.2168771.
33	HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering [C]//Proceedings of the 26th International Conference on World Wide Web. 2017: 173-182. DOI: 10.1145/3038912.3052569.
34	WILLMOTT C J, MATSUURA K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 2005, 30 (1): 79- 82.
35	JÄRVELIN K, KEKÄLÄINEN J. IR evaluation methods for retrieving highly relevant documents [C]//Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2000: 41-48. DOI: 10.1145/345508.345545.
36	WU W, CHEN L, ZHAO Y. Personalizing recommendation diversity based on user personality. User Modeling and User-Adapted Interaction, 2018, 28 (3): 237- 276.
37	MEHTA Y, MAJUMDER N, GELBUKH A, et al. Recent trends in deep learning based personality detection. Artificial Intelligence Review, 2020, 53 (4): 2313- 2339.

模型	数据集	A_Acc	S_AUC	E_MSE
DKT	POJ	0.646	0.538
DKT	LLS			0.0175
E_DKT	POJ	0.648	0.551
E_DKT	LLS			0.0173
Gain/%^⑥		0.3	2.4	1.1

模型	R_HR,5	R_HR,10	G_NDCG,5	G_NDCG,10	P₅	P₁₀
BPRMF	0.182	0.386	0.105	0.170	0.319	0.321
FM	0.178	0.369	0.110	0.171	0.317	0.321
LSTM	0.190	0.380	0.117	0.178	0.317	0.320
DIEN	0.204	0.401	0.120	0.183	0.321	0.322
KPM	0.212	0.414	0.127	0.191	0.34	0.343
Gain/%^⑥	3.9	3.2	5.8	4.4	5.9	6.5

模型	R_HR,5	R_HR,10	G_NDCG,5	G_NDCG,10	P₅	P₁₀
KPM_K	0.190	0.385	0.113	0.175	0.305	0.309
KPM_T	0.193	0.398	0.109	0.175	0.309	0.307
KPM_P	0.162	0.365	0.094	0.159	0.319	0.316
KPM	0.212	0.414	0.127	0.191	0.34	0.343

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[4]	刘恒宇, 张天成, 武培文, 于戈. 知识追踪综述[J]. 华东师范大学学报(自然科学版), 2019, 2019(5): 1-15.
[5]	田志慧，蔡北溟，达良俊. 城市化进程中上海植被的多样性、空间格局和动态响应(Ⅷ): 上海乡土陆生草本植物分布特征及其在城市绿化中的应用前景[J]. 华东师范大学学报(自然科学版), 2011, 2011(4): 24-34.